example of hypothesis in research about social media

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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• Open access
• Published: 22 September 2023

How social media usage affects psychological and subjective well-being: testing a moderated mediation model

• Chang’an Zhang 1 ,
• Lingjie Tang 1 &
• Zhifang Liu 2  

BMC Psychology volume  11 , Article number:  286 ( 2023 ) Cite this article

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A growing body of literature demonstrates that social media usage has witnessed a rapid increase in higher education and is almost ubiquitous among young people. The underlying mechanisms as to how social media usage by university students affects their well-being are unclear. Moreover, current research has produced conflicting evidence concerning the potential effects of social media on individuals' overall well-being with some reporting negative outcomes while others revealing beneficial results.

To address the research gap, the present research made an attempt to investigate the crucial role of social media in affecting students’ psychological (PWB) and subjective well-being (SWB) by testing the mediating role of self-esteem and online social support and the moderation effect of cyberbullying. The data in the study were obtained from a sample of 1,004 college students (483 females and 521 males, M age  = 23.78, SD  = 4.06) enrolled at 135 Chinese universities. AMOS 26.0 and SPSS 26.0 as well as the Process macro were utilized for analyzing data and testing the moderated mediation model.

Findings revealed that social media usage by university students was positively associated with their PWB and SWB through self-esteem and online social support, and cyberbullying played a moderating role in the first phase of the mediation process such that the indirect associations were weak with cyberbullying reaching high levels.

These findings highlight the importance of discerning the mechanisms moderating the mediated paths linking social media usage by young adults to their PWB and SWB. The results also underline the importance of implementing measures and interventions to alleviate the detrimental impacts of cyberbullying on young adults’ PWB and SWB.

Peer Review reports

Introduction

In this digital world, the utilization of social media has become a massive and meaningful part of our everyday life and has grown substantially in recent years [ 1 , 2 ]. People of all ages, adults and adolescents, utilize a diverse array of social media platforms to engage in meaningful connections, both in intimate settings with loved ones and in expansive networks encompassing friends, acquaintances, and professional peers [ 3 ]. It is worth emphasizing that the younger generation is dedicating an ever-growing portion of their time to engaging in online networking platforms, indulging in e-games, exchanging messages, and immersing themselves in various forms of social media [ 4 ]. As a result, there is growing attention among the scholars of social sciences paid to social media research. Despite a handful of studies that have been conducted to shed light on the reasons behind the excessive usage of social media, still literature exploring the potential consequences of utilizing social media is limited, particularly among college students in the context of China. Taking up this research gap, we intend to examine the effects of social media usage on students’ wellbeing, for example, PWB and SWB, which are two distinct but related dimensions of well-being.

Studies on well-being have been grounded on two different philosophical approaches: the hedonic perspective, which defines well-being as the pursuit of pleasure and avoidance of pain, and the eudaimonic perspective, which conceptualizes well-being as the extent to which an individual achieves their potential and experiences personal growth [ 5 ]. Most studies on the hedonic psychological perspective have focused on using SWB measures [ 6 ], whereas the eudaimonic approach, as proposed by Ryff [ 7 ], includes a multidimensional model of PWB consisting of six different aspects of positive functioning: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance [ 8 ]. Although researchers have different approaches, they generally agree that well-being should be understood as a complex concept that incorporates elements from both the hedonic and eudaimonic perspectives [ 5 , 9 ]. Moreover, many scholars recommended that both concepts of wellbeing be re-examined by conducting in-depth and larger research subjects involving diverse cultures and countries [ 10 ]. This is necessary and meaningful since existing studies are typically conducted with subjects in countries referred to as WEIRD (Western, Educated, Industrialized, Rich, Democratic). As such, in this study, we attempted to investigate the impact of social media usage on both PWB and SWB.

Existing literature has revealed that the use of social media is closely related to individuals’ well-being. Some studies found that social media usage can produce beneficial effects. For instance, social media can increase users’ sense of connectedness with others [ 4 ], thus reducing social isolation. Some other studies have demonstrated that engaging in social interactions through smartphones exquisitely enhances one's overall sense of well-being, as it remarkably diminishes feelings of loneliness and shyness [ 11 ] while providing a sense of intimacy [ 12 ], and mobile voice communication with loved ones is a powerful predictor of enhanced PWB [ 13 ]. Furthermore, numerous studies have revealed that the utilization of entertainment-motivated social media can help improve users’ self-disclosure [ 14 ], and facilitated social connections through social media platforms can decrease the sense of stigmatization [ 15 ] and enhance belongingness and social inclusion [ 16 ], contributing to increased SWB. However, some researchers have stressed that social media usage can occasionally divert users' attention from meaningful relationships and hinder social interactions [ 17 , 18 ] and a number of scholars have cautioned against the potential additive relationship with digital devices like smartphones if used excessively [ 12 , 19 ], possibly due to the fear of missing out [ 20 ]. The utilization of social media has unfortunately been linked to a range of distressing consequences including heightened feelings of anxiety [ 21 ], profound loneliness [ 22 ], and debilitating depression [ 23 ]. Additionally, it has been found to perpetuate a sense of social isolation, as well as engender a phenomenon known as "phubbing," whereby individuals become excessively engrossed in their smartphones, thereby compromising genuine interpersonal connections during in-person interactions [ 24 ].

The inconsistent research findings regarding the impact of social media on individuals’ well-being suggest that some factors may play a role in this mechanism. Actually, in addition to the direct association between social media usage and well-being, a number of studies have further identified mediators to investigate underlying mechanisms of this relationship. Previous studies have identified self-esteem and online social support as two promising mediators of the link between social media usage and PWB and SWB. And empirical studies have revealed that media attention and dependency were proven to improve individuals’ self-efficacy [ 25 ], thus increasing their self-esteem. Most importantly, people would rely more on social media, especially during the COVID-19 pandemic in China [ 26 ], to seek social support via the Internet as in-person social support was seriously reduced [ 27 ]. Moreover, social media usage like for informational uses was found to increase people’s self-esteem [ 28 ] and can provide an important avenue for obtaining online social support from friends, peers and important others [ 29 ], which, in turn, reinforce peoples’ PWB and SWB. Although previous studies on mediation effects of self-esteem and online social support have helped elucidate the complex relationship between social media and well-being, further exploration can be made. To test the concurrent mediating effects of self-esteem and online social support, which have been investigated separately in prior studies, would shed more light on the interplay between social media usage and well-being. Furthermore, researchers have acknowledged the importance of exploring the generalizability of their findings to different cultures, like Asian cultures, particularly Chinese culture where collectivism runs strong [ 30 ]. Because previous research indicated that individuals who recorded high collectivism were apt to experience higher levels of well-being, regardless of social media usage [ 15 ], suggesting that a hierarchical society with a strong collectivist culture can play an important role in the impact of people’s social media use on their well-being.

Another factor that intrigued us is cyberbullying. A review of literature on this topic concluded that cyberbully is prevalent on the Internet and some 11.2% to 56.9% of Chinese adolescents reported experiences of cyberbullying victimization, the second-highest median rate among nine nations surveyed in the study [ 31 ]. Similar to traditional bullying, cyberbullying as a victim via social media is founded to be closely related to a series of behavioral and psychological problems (e.g., depression, anxiety, post-traumatic stress disorder, and suicidal ideation) [ 32 , 33 ]. Cyberbullying victimization has also been found to reduce individuals’ self-esteem [ 34 ] and make them feel less inclined to engage with social media platforms and online communities [ 35 ], thus decreasing online social support from peers, friends, and family members. This analysis inspired us to examine whether cyberbullying acts as a moderator in the association between social media usage and well-being. Given the widespread occurrence and undesirable effects of cyberbullying, it is significant for scholars to explore its underlying mechanisms and underexamined consequences. Meanwhile, previous empirical investigations on cyberbullying have largely focused on children and teens [ 36 ]. There have been comparably fewer studies on the influence of cyberbullying on mental health among young adults, like college students, especially in China. In addition, cyberbullying may have a differential impact on adults vs.children. This is particularly true for cyberbullying on social media, as there are differences in the amount of time spent on social media and the specific platforms used by children and adults [ 37 ].

Against the above background and in line with previous studies [ 16 , 38 , 39 , 40 ] we formulated a moderated mediation model to test the roles of self-esteem and online social support as mediators and cyberbullying as a moderator in the relationship of social media and PWB and SWB. Figure  1 presents our moderated mediation model.

Proposed moderated mediation model

Literature review and hypotheses development

Students’ social media usage and well-being.

University students utilize the Internet for various reasons, including leisure activities like participating in online communities or playing games, educational tasks such as completing assignments or applying for scholarships, and practical activities such as researching companies for job interviews. Previous studies have unveiled the rising popularity of social media among students, while more recent investigations have underscored the profound impact that the usage of social media has on their PWB and SWB [ 41 , 42 ]. Research studies have observed a directly or indirectly positive relationship of social media usage with students’ PWB [ 43 , 44 ] and SWB [ 41 , 42 ]. Specifically, PWB serves as a crucial determinant of the overall quality of life, referring to individuals' emotional states and appraisals of their existence [ 45 ], and can include a multiple of dimensions such as autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance [ 8 ]. The utilization of social media by students offers them a broader platform to voice their opinions and emotions regarding their rights, fostering their self-assurance and confidence, and bolstering their knowledge and understanding [ 46 ]. During times of crisis like during the period of COVID-19, the utilization of social media platforms by students presents a valuable avenue for stress relief as they can openly express their thoughts and receive advice from others on how to navigate and overcome the challenging circumstances they find themselves in [ 47 ]. In addition, researchers have also revealed that students’ frequent social media usage to exchange thoughts and strengthen bonds with family and friends can have a positive impact on their PWB by reducing loneliness [ 11 ] and social isolation [ 48 ], and strengthening life satisfaction [ 49 ]. Based on these findings, we can make this hypothesis;

H1a: Social media usage among university students is positively related to their PWB

SWB refers to an individual's overall contentment and happiness, taking into account their personal perception of the significance they place on various aspects of their life. Put simply, SWB encompasses a comprehensive assessment of one's life, encompassing both cognitive evaluations of life satisfaction (cognition) and emotional assessments of feelings and moods (emotion) [ 50 ]. This concept is a growing area of concern in light of the increase in mental health issues in higher education [ 51 ]. A decline in SWB is frequently observed prior to the onset of more severe mental health problems and behavioral issues, including but not limited to depression, suicidal tendencies, and dropping out of college [ 52 , 53 ]. However, some studies have linked social media usage to better SWB. For instance, prior research has demonstrated that social media platforms like Facebook can contribute to users’ accrual of network social capital, thus bolstering SWB [ 54 ]. Also, positive feedback received from individuals with whom one interacts online can significantly enhance overall well-being and mental health. And more frequent quality-based online communication with relatives, friends, family members, and relevant others was also found to have positive impacts on SWB [ 55 ] through lowered depression over time [ 56 ] and enhanced life satisfaction [ 55 ].

Moreover, according to the flow theory, individuals can experience a state of flow when they direct their attention toward accomplishing a specific task or overcoming a challenge in order to attain certain objectives [ 57 ]. This state of flow is characterized by a sense of fulfillment, enhanced cognitive abilities, heightened motivation, and overall happiness [ 58 ]. That is to say, flow improves people’s SWB. To experience a flow state, three conditions need to be fulfilled: having a clear goal and a perceived challenge, maintaining a balance between the difficulty of the challenge and one's skill level, and receiving immediate feedback on progress. Social media, with its enjoyable and controllable nature, provides these conditions and allows users to have an immersive experience, making it a significant source of flow experiences and contributing to people's SWB. In light of this principle, as students increase their usage of social media, they allocate a greater portion of their focus and energy toward engaging with these platforms. In the process of pursuing their objectives, such as engaging in lively conversations with friends via popular messaging applications like WeChat and QQ, or exhibiting their picturesque travel snapshots on platforms like Weibo, they might unexpectedly receive affirming feedback and positive responses from their virtual connections. This immersive and seamless flow experience not only enables individuals to unwind and experience a heightened sense of contentment but also directly enhances their overall sense of SWB. Along this line, we can propose the following hypothesis;

H1b: Social media usage by university students is positively associated with their SWB.

Self-esteem and online social support as mediators

Self-esteem refers to an individual's enduring attitude, whether positive or negative, towards oneself that remains consistent regardless of various circumstances and the passage of time [ 59 , 60 ]. Self-esteem is crucial, especially for young individuals, as they are going through a period of forming their identity, and feedback about themselves can greatly impact their self-esteem [ 61 ]. Research has demonstrated that individuals who possess high self-esteem often experience lower levels of aggressive negative emotions and depression compared to those with low self-esteem [ 62 , 63 ]. Research also revealed that self-esteem functions as an important and positive predictor of PWB and SWB [ 64 ] and success later in life [ 65 ]. By contrast, people who have low self-esteem are likely to be socially anxious, shy, lonely, and introverted. Individuals who experience a decrease in their self-esteem frequently limit their interactions with others, which can impede the formation of close and supportive relationships that are crucial for their overall well-being [ 66 ]. Additionally, they tend to have less stable and satisfying relationships compared to those with high self-esteem [ 67 ]. Furthermore, individuals with low self-esteem tend to engage in self-victimization and shift blame onto others when faced with social failures, rather than acknowledging their own choices. These tendencies lead to avoidance of social interactions, unfamiliar situations, and a general disconnection from society, which in turn heighten the chances of developing social anxiety and depression [ 68 ].

However, interacting with others on social media can generate favorable impacts on one's self-esteem when individuals experience a feeling of belonging and receive encouragement and assistance from their online connections. In the study by Apaolaza et al. [ 69 ], people socializing on social media sites can experience a rise in self-esteem and improvement in their SWB. Moreover, receiving positive feedback on social media can also help boost self-esteem, as others' responses to an individual's posts are usually positive. Studies have shown that the number of likes on social networking sites like Facebook is linked to higher self-esteem [ 70 ]. In more recent research using objective data, it was revealed that Facebook 'likes' have a positive association with happiness, as they boost self-esteem [ 71 ]. Similarly, engaging in self-reflection on social media can have a positive effect on one's self-esteem. By allowing users to carefully select and present information about themselves, social media enables individuals to highlight their positive attributes and experiences, which can boost their self-esteem when they review their profile or past interactions with others [ 40 , 72 ]. As a result, we hypothesized that;

H2a: There exists a mediating role of self-esteem in the relationship between social media usage by university students and their PWB and SWB.

Social support, being one of the most prominent factors that provide protection, plays a crucial and indispensable role in the prevention of mental illnesses [ 73 , 74 ]. It serves as a vital element in safeguarding individuals from the onset and development of psychological disorders [ 75 ]. When individuals received increased levels of social support, they experienced a decrease in feelings of loneliness and an increase in overall happiness [ 76 ]. Online social support refers to the emotional, informational, and instrumental support received through the Internet, as well as the feeling of connection and acceptance from friends, family, and other individuals within one's social circles. Online social support represents the extension of social support that is traditionally available in the physical world to the virtual realm of cyberspace and can enhance the well-being and overall health of individuals, both physically and mentally. This support is facilitated by online platforms and serves as a source of comfort, guidance, and a sense of belonging in times of need. It encompasses various forms of assistance, ranging from empathetic conversations and advice to tangible resources and assistance [ 77 , 78 ]. Through online social support, individuals are able to seek solace, share their experiences, and build meaningful relationships with others, ultimately enhancing their overall well-being and social connectedness in the digital realm. Past research has indicated that the utilization of mobile social media platforms can effectively fortify individuals' connections with others, thus offering them online social support, which in turn aids in the improvement of their well-being [ 79 , 80 ]. A recent review by Gilmour et al. [ 81 ] discovered that using social networking sites like Facebook for seeking social support can enhance users’ overall well-being, as well as improve both physical and mental health. Additionally, it was found to decrease instances of mental illnesses such as depression, anxiety, and loneliness. Thus, online social support seems to have promising effects on young people’s well-being. Along this line, we made the following hypotheses;

H2b: There exists a mediating role of online social support in the relationship between social media usage by university students and their PWB and SWB.

In addition, it has been revealed that self-esteem is a crucial individual factor affecting social support [ 82 ]. Researchers contend that people having greater self-esteem are more inclined to have positive self-evaluations [ 83 ], gain acceptance from others [ 84 ], and exhibit proactive and optimistic behaviors in online contexts [ 85 ]. As a result, they are more likely to receive social support and assistance from their online communities. In comparison, individuals with lower self-esteem typically have negative opinions about themselves, display more negative behavior online, and may not receive as much social support on the Internet [ 86 ]. Furthermore, empirical studies also found a positive relationship between the two variables [ 87 , 88 ]. Given the literature review, we proposed;

H2c: University students’ self-esteem is positively related to their online social support.

Cyberbullying as a moderator

Cyberbullying, according to Rafferty and Vander Ven [ 88 ], was depicted as ‘repeated unwanted, hurtful, harassing, and threatening interaction through electronic communication media’. In contrast to conventional websites, social media platforms provide users with the unique opportunity to selectively share information and content by adjusting their account settings. This remarkable feature has granted young individuals an unprecedented level of access to personal information, as well as a readily accessible platform to exploit this information to their advantage when interacting with others. Cyberbullying can manifest itself across various platforms such as text messages, electronic mail, online chat rooms, and social networking sites. It has emerged as a substantial public health worry due to its potential to induce mental and behavioral health complications, along with an elevated susceptibility to suicidal tendencies [ 89 ]. In fact, cyberbullying poses a detrimental impact on all groups of people who have access to technology, but its consequences are particularly severe for students due to their vulnerable age and susceptibility to online harassment [ 90 ].

According to existing literature, individuals who fall victim to cyberbullying commonly experience a range of psychological issues, including but not limited to stress, depression, feelings of isolation, loneliness, low self-esteem, low academic success, fear of attending school, heightened levels of social anxiety and suicidal ideations [ 91 ]. Furthermore, numerous research studies have consistently demonstrated that cyberbullying inflicts severe emotional and physiological harm upon vulnerable individuals who find themselves unable to defend against such attacks [ 92 ], decreasing their SWB [ 93 ] and causing psychological challenges, such as behavioral issues, alcohol consumption, smoking, and diminished dedication to their academic pursuits [ 94 ]. Due to the detrimental impact of cyberbullying on individuals' well-being, it hinders students' academic success as they struggle to overcome the emotional distress caused by this form of harassment. It was revealed that cyberbullying victimization is strongly associated with various psychological issues such as anxiety, depression, substance abuse, diminished self-esteem, interpersonal difficulties, strained familial relationships, and subpar academic performance among university students [ 95 ].

Research consistently reveals that individuals who are bullied typically have lower levels of self-esteem compared to those who are not victimized [ 34 , 96 ]. And empirical studies based on student samples also confirmed that experience of cyberbullying as a victim was found to be correlated with significantly lower levels of self-esteem [ 94 , 97 ]. In a more recent study based on Chinese university students, Ding et al. [ 98 ] also observed a negative association between cyberbullying and self-esteem. On the other hand, cyberbullying often comes in many forms, such as being ignored, disrespected, threatened, made fun of, and harassed, causing psychological and emotional distress for the victim. Such undesirable feelings and experiences may dampen their motivation and weaken their enthusiasm to engage with online communities [ 35 ], thus decreasing potential online social support they would receive from peers, friends, family members, educators, and romantic partners. Also, cyberbullying erodes the trust individuals have in their online connections so that they would become more cautious about sharing personal information or expressing their thoughts and feelings online [ 99 ], thus hindering the development of genuine connections and limiting the depth of online social support received. In addition, continuous exposure to cyberbullying can damage a person's self-esteem, self-confidence and self-worth, resulting in a wrong belief that they are undeserving of support or that others will not empathize with their experiences [ 95 , 100 ] which may lead to refraining from seeking or accepting online social support. And those suffering from cyberbullying may also choose not to seek online or offline social support due to fear or anxiety, which would in turn have an adverse impact on their well-being [ 101 ].

Based on these findings, it can be inferred that the occurrence of cyberbullying might impact the connection between students' engagement with social media platforms and the positive outcomes it typically fosters. Thus, we hypothesized that;

H3a: Cyberbullying moderates the relationship between social media usage by university students and their self-esteem, wherein the relationship is weaker when cyberbullying is high.

H3b: Cyberbullying moderates the relationship between social media usage by university students and their online social support, wherein the relationship is weaker when cyberbullying is high.

H3c: Cyberbullying moderates the relationship between social media usage by university students and their PWB, wherein the relationship is weaker when cyberbullying is high.

H3d: Cyberbullying moderates the relationship between social media usage by university students and their SWB, wherein the relationship is weaker when cyberbullying is high.

Methodology

Participants and procedure.

The data for the present study were collected via an online survey carried out from April 2023 to May 2023. The survey was based on Wenjuanxing ( www.wjx.cn ), a widely accepted and professional online survey platform for questionnaire design and data collection in China. Questionnaire links can be sent to participants through various social media platforms, such as WeChat, QQ, Weibo, and email. Once the survey is finished, the statistical charts can be downloaded to a Word document for SPSS analysis online, or the original data can be downloaded to Excel and imported into SPSS software for further analysis. It has advantages due to its high efficiency, high quality and low cost. In the present study, questionnaires were designed in Chinese using Wenjuanxing and were then distributed and collected via WeChat and QQ, two popular social platforms that many Chinese people use on a daily basis.

A total of 1,301 active responses were recorded in a response to 1,500 distributed questionnaires (86.73% response rate). Each individual who took part in the research willingly agreed to participate and were given the assurance that their answers would be kept confidential, anonymous, and solely used for the purpose of conducting the study. Since the current study aimed at investigating the influence of social media usage, those who had no access to electronic devices or reported having not used any social media platforms were excluded ( N  = 9). And following careful data cleansing, the final sample comprised 1,004 students, and their major characteristics are displayed in Table 1 . The research participants consisted of both undergraduate (825) and graduate students (179) enrolled in 135 universities and colleges throughout China. Of the total participants, 48.11% were female students and 68.92% were from single-child families. The age range of the sample ranged from 18 to 31 years ( M  = 23.78, SD  = 4.06).

Scale items used in the present study were drawn from the extant literature; thus, well established and validated scales widely applied in prior studies were employed to measure the various constructs in the model shown in Fig.  1 . Given that the respondents in the study are Chinese, the English-language scales used for measuring social media usage and cyberbullying were translated into Chinese. To guarantee that the language was consistent in its meaning, a technique known as back-translation designed by Brislin [ 102 ] was employed. Specifically, this process involved the translation of items from English to Chinese by a bilingual linguist and the back-translation by another bilingual scholar. The other scales we employed were Chinese versions with valid and reliable psychometric properties.

Social media usage scale

In order to assess individuals' engagement on online social platforms, the researchers chose the 9-item general social media usage subscale from the Media and Technology Usage and Attitude Scale (MTUAS) devised by Rosen et al. [ 103 ]. The original MTUAS scale was designed to assess technology and media usage as well as attitudes toward technology. It consists of 60 questions, each of which measures 1 of 11 usage subscales of the questionnaire, and the subscales can be applied collectively or separately. Participants were requested to provide information regarding how often they engage in various activities on social media platforms (e.g., “Read postings; Comment on postings, status updates, photos, etc.”). Each participant assessed the accuracy of the statements using a frequency scale that ranged from 1 ( never ) to 10 ( all the time ) with higher scores indicating more social media usage. According to Rosen et al. [ 103 ] and Barton et al. [ 104 ], the general social media usage scale demonstrated good reliability and validity with the alpha coefficient calculated at 0.97 and 0.90, respectively. In the current study, the measure showed good reliability (Cronbach’s α = 0. 906).

Cyberbullying scale

An instrument devised by Ybarra et al. [ 105 ] captures the prevalence of an individual experiencing aggressive behavior online across various digital media platforms and electronic devices. The four-item self-report scale assesses the frequency of being subjected to such behaviors within the preceding year on a 5-point Likert scale with response options ranging from 1 ( not sure ) to 5 ( often ). Sample statements include: (a) “Someone made a rude or mean comment to me online”, (b) “Someone sent a text message that said rude or mean things”. Higher scores represent greater levels of cyberbullying as a victim. In the present study, the reliability of the scale calculated based on the current sample was high (Cronbach’s α = 0.818).

Self-esteem scale

The Rosenberg Self-Esteem Scale (RSES; Rosenberg, [ 59 ]) was adopted to assess global self-esteem with 10 statements on a 4-point Likert scale. This measure has already been translated into Chinese, demonstrating reliable and adequate psychometric properties [ 85 , 106 ]. Participants’ response categories were set as 1( strongly disagree ) and 4 ( strongly agree ). Example questions include: (a) “I feel that I have a number of good qualities,” and (b) “I take a positive attitude toward myself.” The five negatively worded items on the scale were reverse scored and the height of the scores taken from the measure suggests that a respondent’s self-esteem is high. For the present study, the measure demonstrated good reliability (Cronbach’s α = 0.945).

Online social support scale

The measure of online social support an individual receives was adapted from the Chinese short version of the Online Social Support Scale (OSSS-CS) developed by Zhou and Cheng [ 107 ] as this 20-item instrument has been translated into Chinese and has been tested in Chinese populations demonstrating good internal consistency and high construct validity for its four subscales: esteem/emotional support (0.92), social companionship (0.80), informational support (0.98), and instrumental support (0.92). These four factors were also validated based on confirmatory factor analysis (CFA). Example items include: (a) “People encourage me when I am online”, (b) “People help me learn new things when I am online”, and (c) “When I am online, people help me with school or work”. Participants were asked to rate the frequency of social support in these dimensions they received from the online world and their responses were recorded on a 5-point Likert scale with anchors of 1 ( never ) and 5 ( a lot ). Higher scores indicate greater online social support. In the present study, the measure demonstrated good reliability (Cronbach’s α = 0.956).

The PWB of the participants was evaluated using a shorter Chinese version for Ryff and Keyes’ [ 8 ] PWB Scale [ 108 ]. The 18-item scale is broken down into six different facets: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance. Each aspect was measured by three items and the response to the individual questions was reverse-coded and configured with a 7-point Likert scale, ranging from 1 ( strongly agree ) to 7 ( strongly disagree ). Example items are: (a) “I tend to be influenced by people with strong opinions,” (b) “I have not experienced many warm and trusting relationships with others," and (c) "In many ways I feel disappointed about my achievements in life." Higher scores mean greater PWB. The shortened version scale has been adopted in a series of previous studies on Chinese samples with good internal consistency [ 109 ]. For the current study, the scale was reliable (Cronbach’s α = 0. 959).

The revised version of the College Student SWB Questionnaire (CSSWQ) with 16 self-report items that comprise four subscales was adopted to assess participants’ SWB in terms of academic efficacy, college gratitude, school connectedness, and academic satisfaction [ 53 ]. The four dimensions were measured using four items, respectively, on a 7-point Likert scale with anchors of 1 ( strongly disagree ) and 7 ( strongly agree ). Sample statements are: (a) “I have had a great academic experience at this college,” (b) “I am a diligent student,” and (c) “I feel thankful for the opportunity to learn so many new things." The overall well-being score was calculated by computing the average of all the items on the scale with higher scores reflecting better SWB. This scale has been translated into Chinese and validated on Chinese samples [ 110 ], revealing reliable and valid psychometric properties. In the present study, the measure demonstrated good reliability (Cronbach’s α = 0.953).

Statistical analysis

Before further analyses, we carried out a confirmatory factor analysis (CFA) using AMOS 26.0 to ensure the validity and reliability of the study variables. The potential common method variance (CMV) was checked considering self-report questionnaire was the principal method for obtaining data. After that, data analysis in the study was carried out in three steps using SPSS 26.0. Firstly, descriptive statistics and Pearson’s correlations were summarized and calculated. Then, to test the proposed hypotheses in the study, we employed Haye’s PROCESS macro Model 6 (version 3.4.1 software) [ 111 ] to test the mediating role of self-esteem and online social support in the relationship between social media usage and PWB and SWB. Finally, Haye’s PROCESS macro Model 85 [ 111 ] was conducted to test whether the first stage of indirect relationships and the direct association between social media usage and PWB and SWB was moderated by cyberbullying. In the process, all variables were standardized and the interaction terms were computed from the standardized variables. The bias-corrected percentile bootstrap method and 95% confidence intervals (CI) were applied. If the effect does not include 0 in the 95% CI, it is considered to be statistically significant. Moreover, the simple slope analysis was employed to evaluate the moderating effects [ 112 ]. We plotted the relationship between the independent variable (social media usage) and the dependent variables (self-esteem and online social support) when the levels of the moderator variable (cyberbullying) were one standard deviation below and one standard deviation above mean value of the moderator variable. In addition, demographic variables (i.e., gender, age, family origin) were controlled during the analyses. A p -value of < 0.05 was considered to be statistically significant.

Validity, construct reliability, and common method variance

The content validity and reliability of the study variables analyzed through CFA are displayed in Table 2 . As shown in the table, the item loadings of all factors in the study exceed the threshold value of 0.60 as recommended by Hair et al. [ 113 ]. To ensure the convergent validity of our model, we conducted an analysis of the composite reliability (CR), average variance extracted (AVE), and Cronbach alpha (CA) of all the constructs. The findings from this analysis revealed that the CR and CA values for all the constructs exceeded the recommended threshold of 0.70, indicating a high level of internal consistency. Additionally, construct validity is also confirmed because the AVE values for all the constructs were also above the suggested threshold of 0.50, as advised by previous research studies [ 114 , 115 ]. To assess the discriminant validity of our study, we employed the methodology suggested by Fornell and Larcker [ 114 ]. Our approach involved examining the square root values of AVE for each construct and comparing them with their respective inter-correlations. Considering that the square root of AVE for each factor is greater than its correlations with other factors, it can be concluded that discriminant validity is also established (see Tables 2 and 3 for comparison).

In order to minimize the risk of CMV in our data, we implemented multiple strategies to ensure the accuracy and reliability of the self-reported answers provided by the participants. For instance, as a procedural measure, we took into consideration the suggestions put forward by Podsakoff et al. [ 116 ] to address any potential concerns regarding the anonymity and confidentiality of our participants. We took great care in ensuring our participants that their identities would be kept strictly confidential, and that any information they shared would be treated with the highest level of confidentiality. Additionally, we employed the Herman single-factor test, as recommended by Podsakoff et al. [ 116 ], to evaluate the potential threat of CMV in our study. The results of this test indicated that the first factor accounted for 33.97% of the variance, suggesting that there is no significant problem of CMV present in our study.

Preliminary analyses

Descriptive statistics and correlation matrix between the variables are reported in Table 3 . As expected, all proposed path variables were revealed to be intercorrelated significantly (see Table 3 ). Significant positive correlations were obtained between social media usage and PWB ( r  = 0.40, p  < 0.01) and SWB ( r  = 0.46, p  < 0.01), respectively with large effect sizes. Self-esteem and online social support were found to be positively associated with social media usage ( r  = 0.45, p  < 0.01; r  = 0.43, p  < 0.01), PWB ( r  = 0.54, p  < 0.01; r  = 0.55, p  < 0.01), and SWB ( r  = 0.50, p  < 0.01; r  = 0.53, p  < 0.01), respectively. In addition, cyberbullying was negatively related to self-esteem ( r  = -0.18, p  < 0.01), online social support ( r  = -0.20, p  < 0.01), PWB and SWB ( r  = -0.27, p  < 0.01; r  = -0.16, p  < 0.01), respectively whereas a positive association was observed between this variable and social media usage ( r  = 0.18, p  < 0.01). In general, no significant relationships were identified between the demographic variables and the other variables under investigation. We, therefore, included them as control variables in the follow-up analyses.

Testing for the mediating effect

To test the hypothesized relationship between social media usage and outcomes as well as the mediation of self-esteem and online social support, we utilized SPSS PROCESS macros [ 111 ]. The results presented in Table 4 revealed that social media usage was positively related to self-esteem ( B  = 0.20, t  = 15.75, p  < 0.001), online social support ( B  = 0.09, t  = 7.00, p  < 0.001), PWB ( B  = 0.11, t  = 4.78, p  < 0.001), and SWB ( B  = 0.19, t  = 8.36, p  < 0.001), confirming our hypotheses H1a and H1b. Moreover, the results further showed that self-esteem and online social support mediate the relationship between students’ usage of social media and their PWB and SWB. Specifically, social media usage was significantly and positively associated with PWB via self-esteem (indirect effect = 0.100, SE  = 0.01, 95% CI  = [0.075, 0.126]), via online social support (indirect effect = 0.046, SE  = 0.01, 95% CI  = [0.030, 0.063]), and via self-esteem and online social support (indirect effect = 0.058, SE  = 0.01, 95% CI  = [0.043, 0.074]). Similarly, the utilization of social media by students was also significantly and positively related to their SWB via self-esteem (indirect effect = 0.072, SE  = 0.02, 95% CI  = [0.049, 0.097]), online social support (indirect effect = 0.043, SE  = 0.01, 95% CI  = [0.027, 0.061]), and the two mediators (indirect effect = 0.054, SE  = 0.01, 95% CI  = [0.039, 0.070]). Thus, self-esteem and online social support acted as effective mediators in the association between social media usage and PWB and SWB, supporting H2a, H2b. Moreover, self-esteem had a significant and positive effect on online social support ( B  = 0.57, t  = 19.76, p  < 0.001), thus confirming H2c.

Testing for moderated mediation

In Hypothesis 3, cyberbullying was projected to moderate the first phase of the indirect associations as well as the direct relations between social media usage and PWB and SWB. To test these hypotheses, we performed a moderated mediation analysis by using Haye’s PROCESS macro [ 111 ] in SPSS and investigated Cyberbullying across the levels. Concerning the relationships among study variables, as shown in Table 5 , cyberbullying was negatively correlated with self-esteem ( B  = -0.24, t  = -10.24, p  < 0.001), online social support ( B  = -0.16, t  = -7.16, p  < 0.001), PWB ( B  = -0.30, t  = -7.67, p  < 0.001), and SWB ( B  = -0.19, t  = -4.67, p  < 0.001). The effect of social media usage on self-esteem ( B  = 0.22, t  = 17.69, p  < 0.001) and online social support ( B  = 0.12, t  = 9.12, p  < 0.001) was significant, and more importantly, this effect was moderated by cyberbullying ( B  = -0.11, t  = -7.30, p  < 0.001; B  = -0.10, t  = -6.66, p  < 0.001), respectively. Contrary to our H3c and H3d, the direct relationships between social media usage and PWB ( B  = 0.00, t  = 0.10, p  > 0.05) and SWB ( B  = 0.00, t  = 0.11, p  > 0.05) were not significantly moderated by cyberbullying. Furthermore, the bias-corrected percentile bootstrapping results revealed that the indirect effect of social media usage on PWB via self-esteem (Index of moderated mediation = -0.05, SE  = 0.01, 95% CI  = [-0.07, -0.03]) and online social support (Index = -0.04, SE  = 0.01, 95% CI  = [.-0.06, -0.03]) was moderated by cyberbullying. Likewise, the relationship between social media usage and SWB was indirect and moderated by cyberbullying via self-esteem (Index = -0.04, SE  = 0.01, 95% CI  = [-0.05, -0.02]) and online social support (Index = -0.04, SE  = 0.01, 95% CI  = [-0.06, -0.03]). In addition, results showed that the indirect effects of social media usage by students via self-esteem on their PWB (effect = 0.056, SE  = 0.01, 95% CI  = [0.036, 0.078]) and SWB (effect = 0.041, SE  = 0.01, 95% CI  = [0.024, 0.061]) were weaker at + 1SD than at -1SD (effect = 0.128, SE  = 0.02, 95% CI  = [0.093, 0.165]; effect = 0.094, SE  = 0.02, 95% CI  = [0.061, 0.130]), respectively. Also, a similar pattern was observed for the indirect effects of social media usage via online social support on PWB (effect = 0.019, SE  = 0.01, 95% CI  = [0.003, 0.036]) and SWB (effect = 0.019, SE  = 0.01, 95% CI  = [0.003, 0.037]) at higher level of cyberbullying than at lower level (effect = 0.082, SE  = 0.01, 95% CI  = [0.058, 0.107]; effect = 0.081, SE  = 0.01, 95% CI  = [0.055, 0.107]), respectively. These results have given support to our H3a and H3b.

For clarity, we also plotted graphical diagrams to better examine the role of cyberbullying as a moderator in the relations between social media usage and self-esteem (Fig.  2 ) and online social support (Fig.  3 ), separately for students experiencing low and high cyberbullying (at 1 SD below the mean and 1 SD above the mean, respectively). Simple slope tests suggested that the relationships between social media usage and self-esteem and online social support were statistically weaker respectively when at the higher level of cyberbullying.

Cyberbullying moderates the relationship between social media usage and self-esteem

Cyberbullying moderates the relationship between social media usage and online social support

In this study, a moderated mediation model was formulated to explore whether students’ utilization of social media would be indirectly associated with their PWB and SWB via self-esteem and online social support and whether the first phase of this indirect relationship and the direct correlation would be moderated by cyberbullying they have experienced. Although numerous studies have examined the impacts of social media usage among various groups of people, especially children, this study is one of the few that considers both PWB and SWB as outcome variables among Chinese university students, a sample that has been insufficiently examined. Moreover, this study provides a probable explanation as to why university students' frequent use of social media results in higher levels of PWB and SWB. Moreover, it is the first empirical study confirming the mediating roles of self-esteem and online social support underlying this linkage. The research findings further our understanding of how social media usage impacts users’ well-being and what role cyberbullying plays in the process.

Consistent with our expectations, social media usage by university students positively predicted their PWB and SWB; and self-esteem and online social support mediated the relationships, which extends previous theoretical and empirical studies. Specifically, it helps advance our understanding of the intricate relationship between social media usage and people’s well-being, especially PWB and SWB. Previous research on this association has generated varied results. Some studies have observed a negative relationship while others have acknowledged that a positive association exists as social media can facilitate online social connections [ 117 ] and reduce the levels of negative emotions and feelings, such as stress, loneliness, depression, and the sense of social isolation [ 48 ], thus beneficial to users’ PWB. The research findings suggest that incorporating social media into the daily lives of college students and actively engaging with shared content can have a profound impact on their self-esteem and access to diverse forms of online social support, which, in turn, has the potential to enhance their overall PWB and SWB. In previous empirical studies [ 118 , 119 ], self-esteem was mainly found to be positively correlated with several indicators of SWB including affect, meaning in life, and subjective vitality. The present study contributes to the existing body of research by specifically identifying the positive associations between self-esteem and both PWB and SWB in relation to the usage of social media platforms. In this competitive world, healthy self-esteem is required for university students to effectively deal with potential psychological distress that may arise in their academic and career pursuits. And in accordance with self-affirmation theory, greater self-esteem can work as a buffer against unpleasant and stressful experiences and failures [ 120 ]. Furthermore, Sociometer Theory [ 121 ] suggests that an individual's self-esteem is influenced by their sense of social acceptance and the importance placed on their relationships. This theory provides further insight into the strong correlation between self-esteem and PWB. In collectivistic cultures like China, where social bonds are highly valued, young adults place a great emphasis on their connections with others, particularly within their families and interpersonal relationships. As a result, individuals with higher levels of self-esteem are more likely to experience greater PWB, as their self-esteem serves as a potential indicator of their value within their social circles. In addition to self-esteem, our study also identified positive effects of online social support on students’ well-being consistent with prior research [ 122 ]. The reason behind this phenomenon can be attributed to the fact that students who have a vast network of connections on social media and dedicate a considerable amount of time to actively engaging in various interactions on these platforms are more likely to garner a substantial amount of support from their online acquaintances [ 123 ]. As the number of friends a user possesses increases, the probability of receiving positive and supportive comments on their status updates, appreciation for their uploaded photos, and congratulations for their personal accomplishments also increases. This correlation implies that a larger social circle enhances the likelihood of receiving encouragement and validation from friends. This particular positive experience, which is frequently absent in face-to-face interactions, can strengthen the feeling of being a part of a social network and instill a sense of being valued, respected, and esteemed among students. As a result, it can lead to the development of a positive psychological and emotional state, ultimately contributing to an elevated level of SWB [ 124 ].

Apart from the general mediation effect, it is important to highlight the significance of each individual stage within the mediation process. First, our research finding is in line with prior reports that social media usage increases users' self-esteem [ 69 , 70 ]. Previous research on self-esteem theories has identified a close relationship between the use of various social media sites such as Facebook, Twitter, and Instagram and users’ self-esteem [ 125 , 126 ], revealing that peer interaction and feedback on the self represents critical predictors of young adults’ self-esteem [ 127 ]. In addition to facilitating instant messaging and enabling activities like posting and commenting on photos, social media platforms offer a valuable channel for young people to receive feedback, interact with their peers, enhance their social skills, and gain insights by observing others [ 79 ]. College students in China use similar sites like WeChat and Weibo to portray a different version of themselves online by sharing their photos, videos, and other posts within their friend circles or beyond. The likes they receive on social media sites are regarded as verification for acceptance and approval within their groups of peers, which may, in turn, boost their self-esteem. Since the main objective of social media platforms is to encourage communication and connections between individuals, students who frequently use these sites will have a higher likelihood of actively engaging with their fellow peers and more opportunities to receive positive feedback on social network profiles compared to those who use social media less frequently, thus enhancing their self-esteem. And as predicted, students’ higher self-esteem predicted greater online social support, corresponding to research findings by Jin et al. [ 87 ] and Zheng et al. [ 82 ]. These findings align with the principles of Sociometer Theory [ 84 ], which suggests that there is a strong relationship between self-esteem and how individuals perceive acceptance from society and others. People with high self-esteem often feel valued, which in turn encourages them to engage in positive online communication, receive more affirmation and praise from others, and ultimately be accepted within online communities. On the contrary, individuals who possess low self-esteem often harbor a pessimistic outlook towards their own self-image, leading to more negative online interactions and making it harder for them to receive acceptance from online communities, thus hindering their ability to develop a robust online social support system [ 128 ].

Furthermore, in line with previous research [ 79 , 80 ], our findings indicate that there is a positive correlation between the amount of time students spend on social media and the level of online social support they receive or perceive online. Social support in an online setting has attracted the attention of scholars who have studied its prevalence within social networks. One example of this is when individuals show support for their peers by sharing or forwarding online news articles that would be beneficial to their friends in the digital realm. Moreover, public officials have also recognized the significance of social media in providing updates to citizens during critical events such as natural disasters, criminal incidents, or accidents. In such cases, these officials utilize their social media accounts to keep the public informed and engaged. Additionally, people are able to obtain interpersonal support by connecting and interacting with like-minded individuals on various social media platforms. This form of support, commonly referred to as peer support, serves as a valuable resource for college students seeking understanding, guidance, and empathy from others who share similar interests or experiences [ 129 ]. Moreover, a previous research study conducted on college students found that when seeking social support, students were more inclined to rely on social media platforms rather than seeking help from their parents or mental health professionals. Many of them believed that social media use provided them with positive experiences, offering a support network and helping them feel more connected with their friends. Additionally, the study indicated that students tended to gravitate towards communities composed of their peers who shared similar interests, such as fandom communities [ 130 ]. Building upon a series of similar findings, our study provides new empirical support for the positive effect of social media usage on online social support.

Meanwhile, we identified cyberbullying as a boundary condition variable in our research model. Specifically, the results indicated that the links between social media usage and their PWB and SWB via the two mediators: self-esteem and online social support were weaker for those students suffering greater levels of cyberbullying. In today's technologically advanced society, the issue of online bullying has become a prominent worry in numerous settings. The research we conducted has provided evidence that cyberbullying has the potential to diminish the positive effects that students typically derive from their use of social media. For individuals experiencing a low level of cyberbullying, self-esteem, and online social support can have significant beneficial effects on their PWB and SWB. Increased cyberbullying, however, leads to more psychological distress, reduced life satisfaction, increased depressive symptoms and anxiety [ 131 ], or even suicidal thoughts and attempts [ 132 ]. However, contrary to part of our hypotheses, cyberbullying did not moderate the direct relationship between social media usage and PWB and SWB. A probable explanation for this is that the relationship between social media usage, cyberbullying, and well-being is multifaceted and influenced by various factors. It is possible that other variables not considered in this study could be influencing these relationships. For instance, as evidenced by previous research [ 25 ], cultural and contextual factors like collectivism in Chinese culture can play an important role in the effects of media use on well-being. Meanwhile, as suggested by the Differential Susceptibility to Media Effects Model [ 133 ] and Cultivation Theory [ 134 ], sociocultural and psycho-demographic factors can also moderate social media effects by strengthening, diminishing, and/or moderating individuals’ cognitive, emotional, and behavioral responses to media. Another possible reason is that individuals affected by cyberbullying might have developed coping strategies or mechanisms (e.g., emotion-focused coping and avoidance-coping) to deal with cyberbullying to lessen its impact on their PWB and SWB [ 135 ]. These coping mechanisms might mitigate the expected moderating effect.

Limitations and future directions

The present investigation provides a more comprehensive insight into the intricate relationship between social media usage by Chinese university students and their PWB and SWB and how such relationship is mediated by self-esteem and online social support, and moderated by cyberbullying. However, several limitations should be taken into consideration when analyzing and interpreting the research findings.

First, in our study, we employed a cross-sectional research design, which is not without its limitations, particularly the potential for common method variance (CMV). To address this concern, we implemented various measures, such as guaranteeing the confidentiality and anonymity of participants and conducting statistical analyses to confirm the absence of CMV. Nonetheless, we recognize that our model's credibility and validity could be further strengthened by employing a longitudinal research design or carrying out an experimental laboratory study. Second, it is important to approach the generalizability of the present findings with caution. It remains uncertain whether the findings in our study based on samples collected from Chinese universities can be applied to samples obtained in different contexts, populations (e.g., children, older adults), and countries. Therefore, more studies are warranted to examine these relationships in more diverse samples and contexts since it is noteworthy that social network sites may have different effects on individuals of different ages or nationalities. Third, given our failure to confirm hypotheses regarding cyberbullying moderating the impact of social media usage on PWB and SWB due to possible deficiencies in our research design, it is important to note that future studies should formulate a more comprehensive research design by taking into account a broader context and more factors (e.g., coping strategies, social contexts, cultural norms, and psycho-demographic factors) that may moderate social media impact on health outcomes. Meanwhile, given that some studies have found negative effects of excessive and problematic use of social media on users’ well-being, it is necessary for future studies to examine specific factors resulting in such detrimental outcomes, such as time spent on social media, active or passive social media use [ 136 ], and users’ motives [ 137 ]. Third, the current study found support for the important roles of self-esteem and online social support in explaining why social media usage can be beneficial to users’ PWB and SWB, yet some other factors may also take effect. A more extensive investigation is required in order to gain a comprehensive understanding of the specific circumstances under which predictor variables become significant and the ways in which they interact with online processes and individuals' overall well-being, such as positive and negative emotions while using various social networking sites, bridging and bonding social capital, social connectedness, social comparison, and interpersonal competence. In addition, more studies are needed to determine the circumstances in which social media usage can have positive effects, such as investigating whether social networking platforms that encourage more direct social interaction can improve well-being. Furthermore, future studies can also compare the different roles of direct contact and online contact via different social media platforms in affecting people’s overall well-being. Additionally, it could be further explored how previous experiences with specific social media platforms, potentially influenced by the age of the site and the user, impact the association between usage and PWB and SWB.

Theoretical and practical implications

Despite the limitations, this research has a series of important theoretical and practical implications. First, the current study is one of the few attempts to examine the impact of social media on well-being from both the hedonic and eudaimonic perspectives among university students in the context of China, contributing to the existing literature by empirically confirming the positive implications of social media usage on PWB and SWB. Second, this study extends the extant literature on social media by identifying a mediation pathway that includes self-esteem and online social support, underlying their positive effects. This finding helps shed light on how self-esteem within the theoretical context of Identity Theory and Sociometer Theory can be applied in the digital domain, opening up a new research trajectory to further exploring the effect of various dimensions of self-esteem on health outcomes within the framework of social media research. Also, the examination of online social support as a mediator aligns with communication and media theories that emphasize the importance of technology-mediated communication in shaping relationships and well-being. Moreover, it provides firm support for the Social Compensation hypothesis, which is concerned with how online interaction can generate a host of benefits for individuals struggling with face-to-face interaction due to lack of social skills or low well-being [ 133 ], especially during the pandemic. This can enrich our understanding of how these theories apply within a non-WEIRD cultural context, particularly considering the moderating role of cyberbullying. Lastly, another important contribution of our research is the investigation of the moderating role of cyberbullying, which was found to harm the positive utility of social media on students’ PWB and SWB via diminishing the beneficial effects of self-esteem and online social support. This serves as the core theoretical contribution of this study, adding to the previous body of literature on cyberbullying research, especially its moderating role.

In terms of practical contributions, our results highlight the importance and the beneficial outcomes of social media among college students on their overall well-being. This suggests that educational institutions, teachers, administrators, and parents should recognize the positive application of various social media platforms in academia and encourage rational social media use inside and outside schools. Then the positive effects of self-esteem and online social support indicate that students should communicate and interact more frequently with peers, friends, families and important others as a way to increase their self-esteem and seek more emotional and informational support as well as social companionship. However, the finding that cyberbullying victimization as a moderator can reduce the positive effects of social media usage on health outcomes through mediators of self-esteem and online social support indicates that it is important to empower students at-risk for cyberbullying victimization through prevention efforts. Self-esteem as a social construct is especially influenced by interactions with peers. Hence, it is crucial to offer opportunities for cyberbullying victims to connect with their peers, establish strong relationships, and develop meaningful friendships that contribute to their self-worth and foster a positive self-perception. In addition, as for those enduring cyberbullying-related psychological or behavioral problems (e.g., depression, anxiety, social isolation, and suicidal attempts), most Chinese university counselling centers could open online platforms for psychoeducation like training sessions and courses easily accessible through popular apps, such as WeChat and Tencent [ 138 ], and offer timely and target psychological interventions and counseling. Most importantly, given the prevalence of cyberbullying in China, it is imperative that universities initiate training programs and provide relevant curricula to empower students with basic skills and knowledge to recognize, prevent, and cope with cyberbullying. Bullying tracking software and similar practices can be utilized to prevent cyberbullying while using social media for academic purposes. The authorities may also implement more stringent laws and regulations against cyberbullying and online harassment to create a safe online environment.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Zhang, C., Tang, L. & Liu, Z. How social media usage affects psychological and subjective well-being: testing a moderated mediation model. BMC Psychol 11 , 286 (2023). https://doi.org/10.1186/s40359-023-01311-2

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Social Media Use and Adolescents’ Self-Esteem: Heading for a Person-Specific Media Effects Paradigm

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Patti Valkenburg, Ine Beyens, J Loes Pouwels, Irene I van Driel, Loes Keijsers, Social Media Use and Adolescents’ Self-Esteem: Heading for a Person-Specific Media Effects Paradigm, Journal of Communication , Volume 71, Issue 1, February 2021, Pages 56–78, https://doi.org/10.1093/joc/jqaa039

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Eighteen earlier studies have investigated the associations between social media use (SMU) and adolescents’ self-esteem, finding weak effects and inconsistent results. A viable hypothesis for these mixed findings is that the effect of SMU differs from adolescent to adolescent. To test this hypothesis, we conducted a preregistered three-week experience sampling study among 387 adolescents (13–15 years, 54% girls). Each adolescent reported on his/her SMU and self-esteem six times per day (126 assessments per participant; 34,930 in total). Using a person-specific, N = 1 method of analysis (Dynamic Structural Equation Modeling), we found that the majority of adolescents (88%) experienced no or very small effects of SMU on self-esteem (−.10 < β < .10), whereas 4% experienced positive (.10 ≤ β ≤ .17) and 8% negative effects (−.21 ≤ β ≤ −.10). Our results suggest that person-specific effects can no longer be ignored in future media effects theories and research.

An important developmental task that adolescents need to accomplish is to acquire self-esteem, the positive and relative stable evaluation of the self. Adolescents’ self-esteem is an important predictor of a healthy peer attachment ( Gorrese & Ruggieri, 2013 ), psychological well-being ( Kernis, 2005 ), and success later in life ( Orth & Robins, 2014 ). In the past decade, a growing number of studies have investigated how adolescents’ social media use (SMU) may affect their self-esteem. Adolescents typically spend 2–3 hours per day on social media to interact with their peers and exchange feedback on their messages and postings ( Valkenburg & Piotrowski, 2017 ). Peer interaction and feedback on the self, both bedrock features of social media, are important predictors of adolescent self-esteem ( Harter, 2012 ). Therefore, understanding the effects of SMU on adolescents’ self-esteem is both important and opportune.

To our knowledge, 18 earlier studies have tried to assess the relationship between SMU and adolescents’ general self-esteem (e.g., Woods & Scott, 2016 ) or their domain-specific self-esteem (e.g., social self-concept; Blomfield Neira & Barber, 2014 ; Košir et al., 2016 ; Valkenburg et al., 2006 ). The ages of the adolescents included in these studies ranged from eight to 19 years. Fifteen of these studies are cross-sectional correlational (e.g., Cingel & Olsen, 2018 ; Meeus et al., 2019 ), two are longitudinal ( Boers et al., 2019 ; Valkenburg et al., 2017 ), and one is experimental ( Thomaes et al., 2010 ). Some of these studies have reported positive effects of SMU on self-esteem (e.g., Blomfield Neira & Barber, 2014 ), others have yielded negative effects (e.g., Woods & Scott, 2016 ), and yet others have found null effects (e.g., Košir et al., 2016 ). It is no wonder that the two meta-analyses on the relationship of SMU and self-esteem have identified their pooled relationships as “close to 0” ( Huang, 2017 , p. 351), “puzzling,” and “complicated” ( Liu & Baumeister, 2016 , p. 85).

While this earlier work has yielded important insights, it leaves two important gaps that may explain these weak effects and inconsistent results. A first gap involves the time frame in which SMU and self-esteem have been assessed in previous studies. Inherent to their design, the cross-correlational studies have measured SMU and self-esteem concurrently, at a single point in time. The two longitudinal studies have assessed both variables at three or four times, with one-year lags, with the aim to establish the potential longer-term effects of SMU on self-esteem ( Boers et al., 2019 ; Valkenburg et al., 2017 ). However, both developmental (e.g., Harter, 2012 ) and self-esteem theories (e.g., Rosenberg, 1986 ) argue that, in addition to such longer-term effects, adolescents’ self-esteem can fluctuate on a daily or even hourly basis as a result of their positive or negative experiences. These theories consider the momentary effects of SMU on self-esteem as the building blocks of its longer-term effects. Investigating such momentary effects of SMU on adolescents’ self-esteem is the first aim of this study.

A second gap in the literature that may explain the weak and inconsistent results in earlier work is that individual differences in susceptibility to the effects of SMU on self-esteem have hardly been taken into account. Studies that did investigate such differences have mostly focused on gender as a moderating variable, without finding any effect ( Kelly et al., 2018 ; Košir et al., 2016 ; Meeus et al., 2019 ; Rodgers et al., 2020 ). However, these null findings may be due to the high variance in susceptibility to the effects of SMU within both the boy and girl groups. After all, if differential susceptibility leads to positive effects among some girls and boys and to negative effects among others, the moderating effect of gender at the aggregate level would be close to zero. Therefore, the time is ripe to investigate differential susceptibility to the effects of SMU at the more fine-grained level of the individual rather than by including group-level moderators. Such an investigation would not only benefit media effects theories (e.g., Valkenburg & Peter, 2013 ), but also self-esteem theories that emphasize that the effects of environmental influences may differ from person to person (e.g., Harter & Whitesell, 2003 ). Investigating such person-specific susceptibility to the effects of SMU is, therefore, the second aim of this study.

To investigate the momentary effects of SMU on self-esteem (first aim), and to assess heterogeneity in these effects (second aim), we employed an experience sampling (ESM) study among 387 middle adolescents (13–15 years), whom we surveyed six times a day for three weeks (126 measurements per person). We measured SMU by asking adolescents on each measurement moment how much time in the past hour they had spent on the three most popular social media platforms among Dutch adolescents ( van Driel et al., 2019 ): Instagram, WhatsApp, and Snapchat. We focused on middle adolescence because this is the period of most significant fluctuations in self-esteem ( Harter, 2012 ). By employing a novel, person-specific method to analyze our intensive longitudinal data, we were able, for the first time, to assess the effects of SMU at the level of the individual adolescent, and to assess how these effects differ from adolescent to adolescent.

Social Media Use and Self-Esteem Level

Personality and social psychological research into the antecedents, consequences, and development of self-esteem has mostly focused on two aspects of self-esteem: self-esteem level and self-esteem instability. Most of this research has focused on self-esteem level, that is, whether it is high or low ( Crocker & Brummelman, 2018 ). This also holds for studies into the effects of SMU. For example, all of the 15 correlational studies have investigated whether adolescents who spend more time with social media report a lower (or higher) level of self-esteem compared to their peers who spend less time with social media (e.g., Apaolaza et al., 2013 , 12–17 years; Barthorpe et al., 2020 , 13–15 years; Bourke, 2013 , 12–16 years; Cingel & Olsen, 2018 , 12–18 years; Kelly et al., 2018 , 14 years; Morin-Major et al., 2016 , 12–17 years; O'Dea & Campbell, 2011 , M age 14; Rodgers et al., 2020 , M age 12.8; Thorisdottir et al., 2019 , 14–16 years; Valkenburg et al., 2006 , 10–19 years; van Eldik et al., 2019 , 9–13 years). In statistical terms, these studies have investigated the between -person relationship of SMU and self-esteem.

The majority of studies into the between-person relationship of SMU and self-esteem used Rosenberg’s (1965) self-esteem scale, which is the most commonly used survey measure to assess general, trait-like levels of self-esteem. These studies asked adolescents at one point in time to evaluate their selves in general or across a certain period in the past (e.g., in the past year). In the current study, we also investigated the between-person relationship between SMU and adolescents’ general levels of self-esteem. But unlike earlier studies, we assessed their levels of SMU and self-esteem by averaging the 126 momentary assessments of both variables across a three-week period. Such in situ assessments generally produce data with greater ecological validity because they are made in the natural flow of daily life, which reduces recall bias ( van Roekel et al., 2019 ). Given the inconsistent results in previous studies, the literature does not allow us to formulate a hypothesis on the between-person association between SMU and self-esteem level. Therefore, we investigated the following research question:

(RQ1) Do adolescents who spend more time with social media report a lower or higher level of self-esteem compared to adolescents who spend less time with social media?

Social Media Use and Self-Esteem Fluctuations

A second strand of personality and social psychological research has focused on the instability of self-esteem. Self-esteem instability refers to the extent to which self-esteem fluctuates within persons ( Kernis, 2005 ). Whereas research into the level of self-esteem has predominantly tried to establish differences in self-esteem between persons, work on self-esteem instability has focused on fluctuations in self-esteem within persons. Rosenberg (1986) distinguishes between two types of within-person self-esteem fluctuations: baseline and barometric instability. Baseline instability refers to potential within-person changes in levels of self-esteem that occur slowly and over an extended period of time. It has been shown, for example, that self-esteem decreases in early adolescence after which it may slowly and steadily increase again in later adolescence ( Harter & Whitesell, 2003 ). Barometric fluctuations, in contrast, reflect short-term within-person fluctuations in self-esteem as a result of one’s everyday positive and negative experiences. Rosenberg (1986) argued that such barometric fluctuations are particularly evident during adolescence, when adolescents typically experience enhanced uncertainty about their identity (i.e., how to define who they are and will become), intimacy (i.e., how to form and maintain meaningful relationships), and sexuality (e.g., how to cope with sexual desire and define their sexual orientation; Steinberg, 2011 ).

One of the aims of the current study is to investigate how SMU may induce within-person fluctuations in barometric self-esteem. Two earlier social media effects studies have focused on within-person effects, one longitudinal study ( Boers et al., 2019 , M age 17.7) and one experiment ( Thomaes et al., 2010 , 8–12 years). Using Rosenberg’s self-esteem scale, Boers et al. found negative within-person effects of SMU on baseline self-esteem. However, because the assessments of SMU and self-esteem were one year apart, and because short-term fluctuations can hardly be derived from designs with longer-term measurement intervals ( Keijsers & van Roekel, 2018 ), this study, although important, may not inform a hypothesis on the influences of SMU on barometric self-esteem.

A within-person experiment by Thomaes et al. (2010) does confirm self-esteem instability theories in the context of SMU. Thomaes et al. based their experiment on Leary and Baumeister’s (2000) Sociometer theory. Like Rosenberg’s theory of self-esteem, Sociometer theory proposes that self-esteem serves as a sociometer (cf. barometer) that gauges the degree of approval and disapproval from one’s social environment. An important proposition of Sociometer theory is that self-esteem changes are accompanied by changes in affect (mood and emotions). Self-esteem (and affect) goes up when people succeed or when others accept them, and it drops when people fail or when others reject them. The results of Thomaes et al. confirmed Sociometer theory: When preadolescents’ online social media profiles were approved by others, their self-esteem increased, and when their online profiles were disapproved, their self-esteem dropped.

In Thomaes et al.’s study, peer approval was experimentally manipulated so that one group of preadolescents (8-13 years) received positive feedback and an equally sized group received negative feedback on their online profiles. In reality, however, peer approval and disapproval in social media interactions are typically not as neatly balanced. In fact, studies have often reported a positivity bias in social media-based interactions (e.g., Reinecke & Trepte, 2014 ; Waterloo et al., 2017 ), meaning that social media users tend to share and receive more positive than negative information. This positivity bias also strongly holds for adolescent social media users. For example, among a national sample of adolescents, only 8% “sometimes” received negative feedback on their posts, whereas 91% “never” or “almost never” received such feedback ( Koutamanis et al., 2015 ). Therefore, on the basis of Sociometer theory, the positivity bias of social media interactions, and the findings of Thomaes et al., we expect an overall positive within-person effect of time spent with social media on adolescents’ self-esteem:

(H1) Overall, adolescents’ self-esteem will increase as a result of their time spent with social media in the past hour.

Heterogeneity in the Effects of Social Media Use on Self-esteem

Most media effects theories that have been developed during and after the 1970s agree that media effects are conditional, meaning that they do not equally hold for all media users (for a review see Valkenburg et al., 2016 ). These theories have sparked numerous media effects studies trying to uncover how certain dispositional, environmental, and contextual variables may enhance or reduce the cognitive, affective, and behavioral effects of media. In the past decade, this media effects research has resulted in an upsurge in meta-analyses of media effects, which not only helped integrating the findings in this vastly growing literature, but also pointed at the moderators that may explain differential susceptibility to media effects.

Despite their undeniable value, the effect sizes for both the main and moderating effects of media use that these meta-analyses have yielded typically range between r = .10 and r = .20 ( Valkenburg et al., 2016 ). Although small to medium effect sizes are common in many neighboring disciplines, some media scholars have argued that such small media effects defy common sense because everyday experience offers anecdotal evidence of strong media effects for some individuals ( Valkenburg et al., 2016 ). Moreover, qualitative studies have repeatedly confirmed that media users differ greatly in their responses to (social) media (e.g., Rideout & Fox, 2018 ). And studies on the emotional reactions to scary media content have reported extreme responses for particular individuals ( Cantor, 2009 ).

There is an apparent discrepancy between the magnitude of conditional media effects sizes reported in quantitative studies and meta-analyses on the one hand and the results of qualitative studies and anecdotal examples on the other. By focusing on group-level moderator effects, meta-analyses (and the studies on which they are based) invariably gloss over more subtle individual differences between people ( Pearce & Field, 2016 ). Diving deeper into these subtle individual differences, however, is only possible with research designs that are able to detect differences in person-specific effects. Such designs require a large number of assessments per person to derive conclusions about processes within single persons, as well as a sufficient number of participants for bottom-up generalization to sub-populations ( Voelkle et al., 2012 ).

An important aim of this study is to capture such person-specific susceptibilities to the effects of SMU by employing a novel method of analysis: Dynamic Structural Equation Modeling (DSEM). DSEM is an advanced modeling technique that is suitable for analyzing intensive longitudinal data, that is, data with 20 to more than 100 repeated measurements that are typically closely spaced in time ( McNeish & Hamaker, 2020 ). DSEM combines the strengths of multilevel analysis and Structural Equation Modeling (SEM) with N  =   1 time-series analysis. N  =   1 time-series analysis enables researchers to establish the longitudinal (lagged) associations between SMU and self-esteem within single persons. The multilevel part of DSEM provides the opportunity to test whether the person-specific effect sizes of SMU on self-esteem differ between persons. Combining the power of a large number of assessments of single persons with a large sample, DSEM may help us answer the question: For how many adolescents does SMU support their self-esteem, for how many does it hinder their self-esteem, and for how many does it not affect their self-esteem?

Not only media effects theories, but also self-esteem theories give reason to assume person-specific effects of environmental influences on self-esteem. These theories agree that some individuals experience significant boosts (or drops) in self-esteem when they experience minor disapproval (or approval) from their peers, whereas the self-esteem of others may fluctuate only in case of serious self-relevant experiences ( Crocker & Brummelman, 2018 ). For example, a study by Harter and Whitesell (2003) showed that 59% of adolescents were prone to self-esteem fluctuations, whereas 41% were not or less prone to such fluctuations. Based on these insights of self-esteem theories, it is likely that the effects of SMU will also differ from adolescent to adolescent. Due to the positivity bias of social media interactions, we expect that most adolescents will experience increases in self-esteem as a result of their SMU in the past hour, whereas a smaller group will experience decreases in self-esteem, and for another smaller group of adolescents their SMU will be unrelated to their self-esteem. Therefore, we hypothesize:

(H2) The effect of time spent with social media on self-esteem will vary from adolescent to adolescent.

Participants

This preregistered study is part of a larger project on the psychosocial consequences of SMU. The present study uses data from the first three-week experience sampling method (ESM) wave of this project that took place in December 2019. The sample consisted of 387 early and middle adolescents (13- to 15-year-olds; 54% girls; M age = 14.11, SD = .69) from a large secondary school in the southern area of The Netherlands. Participants were enrolled in three different levels of education: 44% were in lower prevocational secondary education (VMBO), 31% in intermediate general secondary education (HAVO), and 26% in academic preparatory education (VWO). Of all participants, 96% was born in The Netherlands and self-identified as Dutch, 2% was born in another European country, and 2% in a country outside Europe. The sample was representative of this area in The Netherlands in terms of educational level and ethnic background ( Statistics Netherlands, 2020 ).

The study was approved by the Ethics Review Board of the University of Amsterdam. Before the start of the study parents gave written consent for their child’s participation in the study, after they had been extensively informed about the goals of the study. At the end of November 2019, participants took part in a baseline session during school hours. Researchers informed participants of the aims and procedure of the study and assured them that their responses would be treated confidentially. Participants were provided with detailed instructions about the ESM study that started in the week following upon the baseline survey. They were instructed on how to install the ESM software application (Ethica Data) on their phones, and how to answer the different types of ESM questions. At the end of the baseline session, participants completed an initial ESM survey on their use of different social media platforms, which we used to personalize subsequent ESM surveys. In case of questions or problems with the installment of the software, three researchers were present to help out.

ESM study . In the three-week ESM study, participants completed six 2-minute surveys per day in response to notifications from their mobile phones. The first and last ESM surveys contained 24 questions, whereas each of the other four ESM surveys consisted of 23 questions. Each ESM survey assessed, among other variables not reported in this study, participants’ self-esteem and their SMU. Participants received questions about their time spent with Instagram, WhatsApp, and Snapchat if they had indicated in the baseline session that they used these platforms more than once per week. In case participants did not use any of these platforms more than once a week, they were surveyed about other platforms that they did use (e.g., YouTube or gaming). If they did not use any other platforms either, they received other questions to ensure that each participant received the same number of questions. In total, 375 (97%) participants received questions about WhatsApp, 345 participants (89%) about Instagram, and 285 (73%) about Snapchat.

Sampling scheme . In total, participants received 126 ESM surveys (i.e., 21 days * 6 assessments a day) at random time points within fixed intervals. The sampling scheme was tailored to the school’s schedule and participants’ weekday and weekend routines to avoid that participants received notifications during class hours and while sleeping in on the weekends. Five to ten minutes after each ESM notification, participants received an automatic reminder. We have uploaded our entire notification scheme with the response windows on OSF .

Monitoring plan/incentives. We regularly messaged adolescents to check whether we could help with any technical issues and to motivate them to fill out as many ESM surveys as possible. Adolescents received a small gadget for participating in the baseline session, and a compensation of €0.30 for each completed ESM survey. In addition, each day we held a lottery, in which four participants who had completed all six ESM surveys the day before could win €25.

Compliance. We sent out 48,762 surveys (i.e., 387 × 126) to participants. Due to unforeseen technical problems with the Ethica software, 862 ESM surveys did not reach participants. As a result, 47,900 ESM surveys were received, and 34,930 surveys were completed. This led to a compliance rate of 73%, which is good in comparison with previous ESM studies among adolescents ( van Roekel et al., 2019 ). On average, participants completed 90.26 ESM surveys ( SD = 23.84).

A priori power-analyses. The number of assessments was determined based on the fact that a minimum of 50–100 assessments per participant is recommended to conduct N  =   1 time-series analyses ( Voelkle et al., 2012 ). In order to obtain at least 50 assessments per participant, we took a conservative approach and scheduled for a total of 126 assessments. A priori power analyses indicated that a number of 300 participants would suffice to reliably detect small effect sizes with a minimum power of .80 and significance levels of p = .05.

Time spent with social media . To obtain an ecologically valid ESM assessment of time spent with social media, we asked participants at each assessment how much time in the past hour they had spent with the three most popular platforms: WhatsApp, Instagram, and Snapchat. For each platform, we selected the most popular activities ( van Driel et al., 2019 ). For Instagram, we asked: How much time in the past hour have you spent… (1) sending direct messages on Instagram? (2) reading direct messages on Instagram? (3) viewing posts/stories of others on Instagram? For WhatsApp, we asked: How much time in the past hour have you spent… (4) sending messages on WhatsApp? (5) reading messages on WhatsApp? For Snapchat we asked: How much time in the past hour have you spent… (6) viewing snaps of others on Snapchat? (7) viewing stories of others on Snapchat? (8) sending snaps on Snapchat? Response options for each of these activities were measured with a Visual Analog Scale (VAS) that ranged from 0 to 60 minutes with one-minute intervals.

Participants’ scores on these activities were summed for each of the three platforms. For some assessments this summation led to time estimations exceeding 60 min. For WhatsApp this pertained to 0.85% of all 34,127 assessments, for Instagram to 2.40% of all 31,718 assessments, and for Snapchat to 3.87% of all 26,533 assessments. As indicated in our preregistration , these scores were recoded to 60 min. In a next step, the indicated times spent with WhatsApp, Instagram, and Snapchat were summed to create a variable “time spent with social media.” The summation of the three platforms again led to some estimations exceeding 60 min (i.e., 10.64% of all 34,686 estimations). In accordance with our preregistration, these scores were recoded to 60 min.

Self-esteem. Based on Rosenberg’s (1965) self-esteem scale, and studies establishing the validity of single-item measures of self-esteem (e.g., Robins et al., 2001 ), we presented participants with the question: “How satisfied do you feel about yourself right now?” We used a 7-point response scale ranging from 0 (not at all) to 6 (completely), with 3 (a little) as the midpoint.

Method of Analysis

As preregistered , we employed Dynamic Structural Equation Modeling (DSEM) for intensive longitudinal data in Mplus Version 8.4. Following the recommendations of McNeish and Hamaker (2020) , we estimated a two-level autoregressive lag-1 model (AR[1] model) with self-esteem as the outcome. At the within-person level (level 1), we specified SMU in the past hour as the time-varying covariate of self-esteem (to investigate H1), while controlling for the autoregressive effect of self-esteem (i.e., self-esteem predicted by lag-1 self-esteem). At the between-person level (level 2), we included the latent mean level of self-esteem and the latent mean of SMU in the past hour, and the correlation between these mean levels (to investigate RQ1). Finally, we included the between-person variances around the within-person effects of SMU on self-esteem (i.e., random effects to investigate H2).

Before estimating the model, we checked the required assumption of stationarity, that is, whether the mean of the outcome did not systematically change during the study ( McNeish & Hamaker, 2020 ). To do so we compared a two-level fixed effect model with day of study predicting self-esteem with an intercept-only model (i.e., a model without predictors). The assumption of stationarity was confirmed: Day of the study explained only 0.82% of the within-person variance in self-esteem.

Model specifications . By default, DSEM uses Bayesian Markov Chain Monte Carlo (MCMC) for model estimation. We followed our preregistered plan of analyses and ran the DSEM model with a minimum of 5,000 iterations. Before interpreting the estimates, we checked whether the model converged following the procedure of Hamaker et al. (2018) . Model convergence is considered successful when the Potential Scale Reduction (PSR) values are very close to 1 ( Gelman & Rubin, 1992 ), and the trace plots for each parameter look like fat caterpillars. We interpreted the parameters with the Bayesian credible intervals (CIs), as well as the Bayesian p- values. The hypotheses are confirmed if the 95% CIs for the effect of SMU on self-esteem (within-level; H1) and for the variance around this effect (between-level; H2) do not contain 0. Further details of the analytical strategy can be found in the preregistration of the study.

Correlations and Descriptives

Table 1 presents the means, standard deviations (SDs), ranges, and the within-person, between-person, and intra-class correlations (ICCs) of time spent with social media (SMU) and self-esteem. As the table shows, the average level of self-esteem was high ( M  =   4.09, SD = 1.12, range = 0–6). Participants spent on average almost 17 minutes (range 0–60 min.) with social media in the hour before each measurement occasion. The between-person association of the mean level of SMU with the mean level of self-esteem was significantly negative ( r = −.14, p = .005). The within-person correlation was close to zero ( r = −.01, p = .028), but significant (due to the high power of the study).

Descriptive Statistics and Within-Person, Between-Person, and Intra-Class Correlations of Time Spent with Social Media (SMU) and Self-Esteem

Mean scores reflect average number of minutes spent with social media in the past hour.

Within-person association ( p = .028) between SMU and self-esteem.

between-person association ( p = .005) between SMU and self-esteem.

The Intra-Class Correlations (ICCs) were .45 for self-esteem and .48 for SMU, which means that 45% of the variance in self-esteem and 48% of the variance in SMU was explained by differences between participants (i.e., between-person variance), whereas the larger part of these variances (55% and 52%) was explained by fluctuations within participants (i.e., within-person variance). These ICCs confirm that our sampling scheme of six assessments a day was appropriate for assessing within-person fluctuations in self-esteem and SMU and led to data with sufficient within-person variance for DSEM analyses.

DSEM Results

In all the steps of the analysis strategy, we followed our preregistered plan . We first ran a DSEM model with a minimum of 5,000 iterations (and a default maximum of 50,000 iterations) and one-hour time intervals (TINTERVAL = 1). This model did not converge: The Potential Scale Reduction (PSR) convergence criterion reached 1.354, which is not close enough to 1. As recommended by McNeish and Hamaker (2020) , in a next step, we improved the model setup by increasing the time interval from 1 to 2 hours (TINTERVAL = 2). This model converged well and before the 5,000 iterations. The PSR for this model was 1.006. Visual inspection of the trace plots confirmed that convergence was successful. Finally, we also ran a model with 10,000 iterations to exclude the possibility that the PSR value of 5,000 iterations was close to 1 by chance ( Schultzberg & Muthén, 2018 ). This model reached a PSR of 1.002, and its results did not deviate from the model with 5,000 iterations.

Investigating Research Question and Hypotheses

To answer our research question (RQ1), we investigated the between-person association between SMU and self-esteem. The DSEM analyses revealed a significantly negative association of −.147 between SMU and participants’ level of self-esteem, meaning that participants who spent more time with social media across the three weeks had a lower average level of self-esteem compared to participants who spent less time with social media across this period ( Table 2 ).

DSEM Results of the Between-Person Associations and Within-Person Effects of Time Spent with Social Media (SMU) and Self-Esteem (S-E)

The relationship between SMU and β rβ reflects the extent to which the within-person effect of momentary SMU on momentary S-E depends on the average level of adolescents’ SMU;

The relationship between S-E and β β reflects the extent to which the within-person effect of momentary SMU on momentary S-E depends on adolescents’ average level of S-E;

The 95% Credible Interval of the variance around the effect of SMU on S-E indicates that the within-person effect of SMU on S-E differed among participants. b ’s are unstandardized; β β’s are standardized using the STDYX Standardization in Mplus; p -values are one-tailed Bayesian p -values ( McNeish & Hamaker, 2020 ).

Our first hypothesis (H1) predicted an overall positive within-person effect of SMU on self-esteem. This within-person effect represents the average changes in self-esteem (i.e., self-esteem controlled for self-esteem at t −1) as a result of SMU in the previous hour. This hypothesis did not receive support. Despite the high power of the study, the within-person effect was nonsignificant (β = −.009), meaning that, on average, participants’ self-esteem did not increase nor decrease as a result of their SMU in the previous hour ( Table 2 ).

Our second hypothesis (H2), which predicted that the within-person effect of SMU on changes in self-esteem would differ from participant to participant, did receive support ( Table 2 : random effect = 0.006, p = .000). This random effect means that there was significant variance between participants in the extent to which their SMU in the previous hour predicted changes in their self-esteem.

Figure 1 shows the distribution of the person-specific standardized effect sizes for the effect of SMU on changes in self-esteem. These effect sizes ranged from β = −.21 to β = +.17 across participants. As the bar graph shows, the majority of participants (88%) experienced no or very small positive or negative effects of their SMU (i.e., −.10 < β < .10) on changes in self-esteem, whereas a small group of participants (4%) experienced positive (.10 ≤ β ≤ .17), and another small group (8%) experienced negative effects (−.21 ≤ β ≤ -.10) of SMU on changes in self-esteem. Figure 2 presents the N  =   1 time-series plots of three participants, one who experienced a positive, one who experienced a negative, and one who experienced a null-effect of SMU on self-esteem.

Range of the Standardized Person-Specific Effects of SMU on in Self-Esteem.

Note. The vertical black line represents the mean of the person-specific effects ( β = −.009).

Three N = 1 time-series plots picturing the effects of SMU on self-esteem (S-E).

Note . The x -axes represent the measurement moments (range 1–126). The y -axes represent the co-fluctuations in SMU (blue lines, range 0–60 minutes/10) and S-E (yellow lines, range 0–6). The top plot belongs to a participant who experienced a positive effect of SMU on S-E ( β = .174). The SMU and S-E of this participant regularly co-fluctuated (e.g., around moment 40 and around moment 41). The middle plot is from a participant who experienced a negative effect ( β β = −.196): When the SMU of this participant increased, his/her S-E dropped (e.g., around moment 56), and vice versa (e.g., around moment 21). The bottom plot is from a participant who experienced no effects ( β = .013): At some moments, the S-E of this participant increased after his/her SMU increased (e.g., around moment 45), at othermoments her/his S-E dropped after his/her SMU went up (e.g., moment 72), resulting in a net effect close to zero.

Exploratory Analyses

In addition to our preregistered hypotheses, we ran four exploratory analyses. In a first step, we investigated potential platform differences. Because earlier studies into the relationship between SMU and self-esteem did not investigate differential effects of different platforms, we summed adolescents’ use of Instagram, Snapchat, and WhatsApp to create our SMU measure. To explore potential platforms differences, we reran our analyses separately for each of the three platforms. Our results did not show significant differences in the between-person relationships and within-person effects of the use of these platforms on self-esteem (see Supplement 1).

In a second step, we ran a multilevel model without controlling for self-esteem at the previous assessment. Given that DSEM models are rather stringent and that sizeable differences in effect sizes between lagged and non-lagged media effects have been reported ( Adachi & Willoughby, 2015 ), we wanted to get insight into these differences. All other model specifications of the multilevel model were identical to the initial DSEM model. The associations between SMU and self-esteem in the multilevel model ranged from β = −.34 to β = +.33. Consistent with the DSEM model, the average within-person association of SMU and self-esteem was close to zero (β = −.007, p = .162, CI = [−0.022, 0.007] compared to β = −.009 in the DSEM model).

In a third step, we explored whether the person-specific within-person effects of SMU on self-esteem (i.e., the βs) differed for adolescents with different mean levels of SMU or different mean levels of self-esteem. As Table 2 shows, the cross-level interaction of participants’ mean levels of SMU with the β’s was non-significant, indicating that adolescents with higher mean levels of SMU did not experience a more negative (or positive) within-person effect of SMU on their self-esteem than their peers with lower SMU. The cross-level interaction of self-esteem and the βs did reveal that the within-person effect of SMU on self-esteem depended on adolescents’ mean level of self-esteem: Adolescents with lower average levels of self-esteem had a more positive within-person effect of SMU on self-esteem than adolescents with higher average levels of self-esteem, and vice versa.

In a final step, we investigated a between-person hypothesis of one of the anonymous reviewers, who suggested to check whether adolescents with moderate SMU would experience higher trait levels of self-esteem than those with low and high SMU. We investigated this potential inverted U-shaped relationship between SMU and self-esteem by following the two-step hierarchical regression analysis used by Cingel and Olsen (2018) . At step 1 of this regression analysis, we found a negative linear relationship between SMU and self-esteem (β = − .145, p = .005; R 2 = .021, see also Table 1 ). At step 2, we found no significant curvilinear relationship between SMU and self-esteem, because the added squared SMU term did not result in a significant change in the explained variance (Δ R 2 = .001, Δ F (1, 380) = .516, p = .473).

Sensitivity Analysis

As preregistered , we conducted a validation check to examine whether participants’ answers were trustworthy according to the following criteria: (1) inconsistency of participants’ within-person response patterns, (2) outliers, (3) unserious responses (e.g., gross comments) to the open question in the ESM study. Based on these criteria, we considered the responses of eight participants as potentially untrustworthy, because they violated criterion 1 and 2 ( n  =   4) or criterion 1 and 3 ( n  =   4). As a sensitivity analysis, we reran the DSEM analysis without these eight participants. The results of both the between-person and within-person associations did not deviate from those of the full sample.

The two existing meta-analyses on the relationship of SMU and self-esteem assessed the effects of their included empirical studies as weak and their results as mixed ( Huang, 2017 ; Liu & Baumeister, 2016 ). The between-person associations reported in empirical studies on SMU and self-esteem ranged from +.22 ( Apaolaza et al., 2013 ) to − .28 ( Rodgers et al., 2020 ). In the current study, the between-person association between SMU and self-esteem fits within this range: We found a negative relationship of r = − .15 between SMU and self-esteem (RQ1), meaning that adolescents who spent more time on social media across a period of three weeks reported a lower level of self-esteem than adolescents who spent less time on social media. This negative relationship pertained to the summed usage of Instagram, Snapchat, and WhatsApp, but did not differ for the usage of each of the separate platforms.

In addition, although we hypothesized a positive overall within -person effect of SMU on self-esteem (H1), we found a null effect. However, this overall null effect must be interpreted in light of the supportive results for our second hypothesis (H2), which predicted that the effect of SMU on self-esteem would differ from adolescent to adolescent. We found that the majority of participants (88%) experienced no or very small positive or negative effects of SMU on changes in self-esteem ( − .10 < β < .10), whereas one small group (4%) experienced positive effects (.10 ≤ β ≤ .17), and another small group (8%) negative effects of SMU ( − .21 ≤ β ≤ − .10) on self-esteem.

The person-specific effect sizes reported in the current study pertain to SMU effects on changes in self-esteem (i.e., self-esteem controlled for previous levels of self-esteem). As Adachi and Willoughby (2015 , p. 117) argue, such effect sizes are often “dramatically” smaller than those for outcomes that are not controlled for their previous levels. Indeed, when we checked this assumption of Adachi & Willoughby, the associations between SMU and self-esteem not controlled for its previous levels resulted in a considerably wider range of effect sizes (β = − .34 to β = +.33) than those that did control for previous levels (β = − . 21 to β = +.17). To account for a potential undervaluation of effect sizes in autoregressive models, Adachi and Willoughby (2015 , p. 127) proposed “a more liberal cut-off for small effects in autoregressive models (e.g., small = .05).” In this study, we followed our preregistration and interpreted effect sizes ranging from − .10 < β < +.10 as non-existent to very small. However, if we would apply the guideline proposed by Adachi and Willoughby (2015) to our results, the distribution of effect sizes would lead to 21% negative susceptibles, 16% positive susceptibles, and 63% non-susceptibles.

Our results showed that the effects of SMU on self-esteem are unique for each individual adolescent, which may, in turn, explain why the two meta-analyses evaluated the effects of their included studies as weak and their results as inconsistent. First, our results suggest that these effects were weak because they were diluted across a heterogeneous sample of adolescents with different susceptibilities to the effects of SMU. This suggestion is supported by comparing our overall within-person effect (β = − .01, ns) with the full range of person-specific effects, which ranged from moderately negative to moderately positive. Second, the effects reported in earlier studies may have been inconsistent because these studies may, by chance, have slightly oversampled either “positive susceptibles” or “negative susceptibles.” After all, if a sample is somewhat biased towards positive susceptibles, the results would yield a moderately positive overall effect. Conversely, if a sample is somewhat biased towards negative susceptibles the results would report a moderately negative overall effect.

It may seem reassuring at first sight that the far majority of participants in our study did not experience sizeable negative effects of SMU on their self-esteem. However, as illustrated in the bottom N  =   1 time-series plot in Figure 2 , for some participants, their non-significant within-person effect may result from strong social media-induced ups and downs in self-esteem, which cancelled each other out across time, resulting in a net null effect. However, as the two upper time-series plots in Figure 2 show, not only the non-susceptibles, but also the positive and negative susceptibles sometimes experienced effects in the opposite direction: The positive susceptibles occasionally experienced negative effects, while the negative susceptibles occasionally experienced positive effects.

Although DSEM models enable researchers to demonstrate how within-person effects of SMU differ across persons, they do not (yet) allow us to statistically evaluate the presence of both positive and negative effects within one and the same person (Hamaker, 2020, personal communication). A possibility to analyze the combination of positive and negative effects within persons may soon be offered by even more advanced modeling strategies than DSEM, which are currently undergoing a rapid development. Among those promising developments are regime switching models ( Lu et al., 2019 ), which provide the opportunity to establish the co-occurrence of both positive and negative effects of SMU within single persons.

Explanatory Hypotheses and Avenues for Future Research

Although our study allowed us to reveal the prevalence of positive susceptibles, negative susceptibles, and non-susceptibles among participants, it did not investigate why and when some adolescents are more susceptible to SMU than others. Our exploratory results did show that adolescents with a lower mean level of self-esteem, experienced a more positive within-person effect of SMU on self-esteem than adolescents with a higher mean level of self-esteem. This latter result may point to a social compensation effect ( Kraut et al., 1998 ), indicating that adolescents who are low in self-esteem may successfully seek out social media to enhance their self-esteem. Our DSEM analysis did not reveal differences in the within-person effects of SMU on self-esteem among adolescents with high and low SMU, suggesting that the positive effects among some adolescents cannot be attributed to modest SMU, whereas the negative effects among other adolescents cannot be attributed to excessive SMU.

An important next step is to further explain why adolescents differ in their susceptibility to SMU. A first explanation may be that adolescents differ in the valence (the positivity or negativity) of their experiences while spending time on social media. It is, for example, possible that the positive susceptibles experience mainly positive content on social media, whereas the negative susceptibles experience mainly negative content. In this study, we focused on time as a predictor of momentary ups and downs in self-esteem. However, most self-esteem theories emphasize that it is the valence rather than the duration of social experiences that results in self-esteem fluctuations. It is assumed that self-esteem goes up when we succeed or when others accept us, and drops when we fail or when others reject us ( Leary & Baumeister, 2000 ). Future research should, therefore, extend our study by investigating to what extent the valence of experiences on social media accounts for differences in susceptibility to the effects of SMU above and beyond adolescents’ time spent on social media.

A second explanation as to why adolescents differ in their susceptibility to the effects of SMU may lie in person-specific susceptibilities to the positivity bias in SM. Our first hypothesis was based on the idea that the sharing of positively biased information would elicit reciprocal positive feedback from fellow users, which, in turn, would lead to overall improvements in self-esteem. However, our results suggest that, for some adolescents, this positivity bias may lead to decreases in self-esteem, for example, because of their tendency to compare themselves to other social media users who they perceive as more beautiful or successful. This tendency towards social comparison may lead to envy (e.g., Appel et al., 2016 ) and decreases in self-esteem ( Vogel et al., 2014 ).

Until now, studies investigating the positive feedback hypothesis have mostly focused on the positive effects of feedback on self-esteem (e.g., Valkenburg et al., 2017 ), whereas studies examining the social comparison hypothesis have mainly focused on the negative effects of social comparison on self-esteem (e.g., Vogel et al., 2014 ). However, both the positive feedback hypothesis and the social comparison hypothesis are more complex than they may seem at first sight. First, although most adolescents receive positive feedback while using social media, a minority frequently receives negative feedback ( Koutamanis et al., 2015 ), and may experience resulting decreases in self-esteem. Likewise, although social comparison may lead to envy, it may also lead to inspiration (e.g., Meier & Schäfer, 2018 ), and resulting increases in self-esteem. Future research should attempt to reconcile these explanatory hypotheses by investigating who is particularly susceptible to positive and/or negative feedback, and who is particularly susceptible to the positive (e.g., inspiration) and/or negative (e.g., envy) effects of social comparison on social media.

Another possible explanation for differences in person-specific effects of SMU on self-esteem may lie in differences in the specific contingencies on which adolescents’ self-esteem is based. Self-esteem contingency theory ( Crocker & Brummelman, 2018 ) recognizes that people differ in the areas of life that serve as the basis of their self-esteem ( Jordan & Zeigler-Hill, 2013 ). For example, for some adolescents their physical appearance may serve as the basis of their self-esteem, whereas others may base their self-esteem on peer approval. Different contexts may also activate different self-esteem contingencies ( Crocker & Brummelman, 2018 ). On the soccer field, athletic ability is valued, which may activate the athletic ability contingency in this context. On social media, physical appearance and peer approval may be relevant, so that these contingencies may particularly be triggered in the social media context. It is conceivable that adolescents who base their self-esteem on appearance or peer approval may be more susceptible to the effects of SMU than adolescents who base their self-esteem less on these contingencies, and this is, therefore, another important avenue for future research.

Stimulating Positive and Mitigating Negative Effects

Our results suggest that for the majority of adolescents the momentary effects of SMU are small or negligible. As discussed though, all adolescents—whether they are positive susceptibles, negative susceptibles, or non-susceptibles—may occasionally experience social media-induced drops in self-esteem. Social media have become a fixture in adolescents’ social life, and the use of these media may thus result in negative experiences among all adolescents. Therefore, not only the negative susceptibles, but all adolescents need their parents or educators to help them prevent, or cope with, these potentially negative experiences. Parents and educators can play a vital role in enhancing the positive effects of SMU and combatting the negative ones. Helping adolescents prevent or process negative feedback and explaining that the social media world may not be as beautiful as it often appears, are important ingredients of media-specific parenting as well as school-based media literacy programs.

Although this study was designed to contribute to (social) media effects theories and research, our analytical approach may also have social benefits. After all, N  =   1 time-series plots could not only be helpful for theory building, but also for person-specific advice to adolescents. These plots give a comprehensive snapshot of each adolescent’s experiences and responses across more or less prolonged time periods. Such information could greatly help tailoring prevention and intervention strategies to different adolescents. After all, only if we know which adolescents are more or less susceptible to the negative and positive effects of social media, are we able to adequately target prevention and intervention strategies at these adolescents.

Towards a Personalized Media Effects Paradigm

Insights into person-specific susceptibilities to certain environmental influences is burgeoning in several disciplines. For example, in medicine, personalized medicine is on the rise. In education, personalized learning is booming. And in developmental psychology, differential susceptibility theories are among the most prominent theories to explain heterogeneity in child development. Although N  =   1 or idiographic research is now progressively embraced in multiple disciplines, spurred by recent methodological developments, it has a long history behind it. In fact, in the first two decades of the 20th century, scholars such as Piaget, Pavlov, and Thorndike often conducted case-by-case research to develop and test their theories bottom up (i.e., from the individual to the population; Robinson, 2011 ). However, in the 1930s, idiographic research soon lost ground to nomothetic approaches, certainly after Francis Galton attached the term nomothetic to the aggregated group-based methodology that is still common in quantitative research ( Robinson, 2011 ). However, due to technological advancements, it has become feasible to collect masses of intensive longitudinal data from masses of individuals on the uses and effects of social media (e.g., through ESM, tracking). Moreover, rapid developments in data mining and statistical methods now also enable researchers to analyze highly complex N  =   1 data, and by doing so, to develop and investigate media effects and other communication theories bottom-up rather than top-down (i.e., from the population to the individual). We hope that this study may be a very first step to a personalized media effects paradigm.

Additional Supporting Information may be found in the online version of this article.

This study was funded by an NWO Spinoza Prize and a Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to Patti Valkenburg by the Dutch Research Council (NWO). Additional funding was received from a VIDI grant (NWO VIDI Grant 452.17.011) awarded to Loes Keijsers.

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ORIGINAL RESEARCH article

Effects of social media use on psychological well-being: a mediated model.

• 1 School of Finance and Economics, Jiangsu University, Zhenjiang, China
• 2 Research Unit of Governance, Competitiveness, and Public Policies (GOVCOPP), Center for Economics and Finance (cef.up), School of Economics and Management, University of Porto, Porto, Portugal
• 3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan
• 4 CETYS Universidad, Tijuana, Mexico
• 5 Department of Business Administration, Al-Quds University, Jerusalem, Israel
• 6 Business School, Shandong University, Weihai, China

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Introduction

The use of social media has grown substantially in recent years ( Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” ( Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media ( Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction ( Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction ( Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction ( Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out ( Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others ( Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers ( Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities ( Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel ( Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas ( Carlson et al., 2016 ), which consequently may be significantly correlated to social support ( Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage ( Karikari et al., 2017 ), particularly regarding its societal implications ( Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts ( Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam (1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen (2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam (1995 , 2000) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties ( Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being ( Bano et al., 2019 ). Indeed, Williams (2006) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital ( Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen (2014) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions ( Chen and Li, 2017 ). Abbas and Mesch (2018) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. (2017) also found positive effects of social media use on social capital. Similarly, Pang (2018) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. (2019) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim (2017) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being ( Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities ( Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” ( Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity ( Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities ( Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation ( Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation ( Whaite et al., 2018 ).

Chappell and Badger (1989) stated that social isolation leads to decreased psychological well-being, while Choi and Noh (2019) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. (2012) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” ( Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices ( Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction ( Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones ( Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction ( Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart ( Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities ( Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others ( Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” ( Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing ( Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing ( Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. (2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity ( Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being ( Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

Figure 1 . Conceptual model.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context ( Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones ( Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents ( Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research ( Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data ( Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) ( Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

Table 1 . Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. (2017) . Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan (2015) . Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh (2019) . Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban (2013) . Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas (2018) . Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. (2017) . Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields ( Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” ( Sarstedt and Cheah, 2019 ). According to Ringle et al. (2015) , this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah (2019) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. (2019) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data ( Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 ( Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske (1959) , who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey ( Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings ( Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results ( Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB ( Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold ( Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB ( Hair et al., 2019 ). Hair et al. (2019) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. (1991) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 ( Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

Table 2 . Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability ( Hair et al., 2012 ). Hair et al. (2017) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. (2019) . According to Nunnally (1978) , Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi (1988) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker (1981) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker (1981) , the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

Table 3 . Study measures, factor loading, and the constructs' reliability and convergent validity.

Table 4 . Discriminant validity and correlation.

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. (2019) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power ( Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen (1998) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's (1998) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

Table 5 . Summary of path coefficients and hypothesis testing.

Figure 2 . Structural model.

Table 6 . Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results ( Ringle et al., 2012 ). Hair et al. (2019) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively ( Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. (2019) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit ( Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's (2008) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes (2008) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. (2018) , if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes (2008) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan (2015) and Ellison et al. (2007) , who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. (2021) , who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan (2015) and Karikari et al. (2017) . Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. (2019) , who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li (2017) .

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation ( Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar (2020) . The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh (2019) , social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. (2016) , who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. (2016) , Salehan and Negahban (2013) , and Swar and Hameed (2017) . The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. (2019) , who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat (2019) , who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee (2020) , Chotpitayasunondh and Douglas (2016) , Guazzini et al. (2019) , and Tonacci et al. (2019) , who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas (2018) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. (2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression ( Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. (2018) , who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim (2017) , who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections ( Putnam, 1995 , 2000 ) with heterogeneous weak ties ( Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties ( Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This study is supported by the National Statistics Research Project of China (2016LY96).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Citation: Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM and Liu F (2021) Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Front. Psychol. 12:678766. doi: 10.3389/fpsyg.2021.678766

Received: 10 March 2021; Accepted: 25 May 2021; Published: 21 June 2021.

Reviewed by:

Copyright © 2021 Ostic, Qalati, Barbosa, Shah, Galvan Vela, Herzallah and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sikandar Ali Qalati, sidqalati@gmail.com ; 5103180243@stmail.ujs.edu.cn ; Esthela Galvan Vela, esthela.galvan@cetys.mx

† ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342 Sikandar Ali Qalati orcid.org/0000-0001-7235-6098 Belem Barbosa orcid.org/0000-0002-4057-360X Esthela Galvan Vela orcid.org/0000-0002-8778-3989 Feng Liu orcid.org/0000-0001-9367-049X

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• Published: 05 March 2020

Social media, nature, and life satisfaction: global evidence of the biophilia hypothesis

• Chia-chen Chang 1   na1 ,
• Gwyneth Jia Yi Cheng 1   na1 ,
• Thi Phuong Le Nghiem 1 ,
• Xiao Ping Song   ORCID: orcid.org/0000-0002-8825-195X 2 , 4 ,
• Rachel Rui Ying Oh   ORCID: orcid.org/0000-0003-2716-7727 3 ,
• Daniel R. Richards   ORCID: orcid.org/0000-0002-8196-8421 4 &
• L. Roman Carrasco   ORCID: orcid.org/0000-0002-2894-1473 1  

Scientific Reports volume  10 , Article number:  4125 ( 2020 ) Cite this article

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Humans may have evolved a need to connect with nature, and nature provides substantial cultural and social values to humans. However, quantifying the connection between humans and nature at a global scale remains challenging. We lack answers to fundamental questions: how do humans experience nature in different contexts (daily routines, fun activities, weddings, honeymoons, other celebrations, and vacations) and how do nature experiences differ across countries? We answer these questions by coupling social media and artificial intelligence using 31,534 social media photographs across 185 countries. We find that nature was more likely to appear in photographs taken during a fun activity, honeymoon, or vacation compared to photographs of daily routines. More importantly, the proportion of photographs with nature taken during fun activities is associated with national life satisfaction scores. This study provides global evidence of the biophilia hypothesis by showing a connection between humans and nature that contributes to life satisfaction and highlights how nature serves as background to many of our positive memories.

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Introduction.

Ecosystems provide multiple benefits to humans, encompassing economic, ecological, cultural, and social values 1 , 2 . Despite these benefits, continuing environmental degradation has placed millions of animal and plant species under risk of extinction 3 , 4 . Removal and degradation of natural environments is expected to have negative consequences on human wellbeing 5 . This disparity between the overexploitation of natural resources and its importance to humans stems largely from the difficulty in integrating the value of nature’s benefits to people (“ecosystem services”) into policy 6 .

The value of ecosystem services is complex and multifaceted 6 . Although significant progress has been made in the economic and ecological valuation of ecosystem services, much less attention has been paid to cultural and social values, which are the most complex to capture 7 . Cultural ecosystem services are intangible benefits that people gain from experiencing nature 7 , 8 . The concept of “nature” is amorphous, so here we define nature as including biodiversity, ecosystems, living organisms, landscapes, and seascapes 5 . Nature provides an environmental space for cultural practices (including interacting with nature directly or using nature as background for other social activities) and yields various benefits 9 . These benefits include, among others, spiritual experiences, recreation, ecotourism, aesthetic appreciation, and further improved social cohesion and subjective wellbeing 5 , 6 , 7 , 8 , 9 .

Quantifying cultural ecosystem services is challenging as they represent immaterial benefits and the assessment involves untangling the reasons behind why people enjoy a particular space 7 , 10 . Collecting such information involves surveys or interviews that are resource-intensive and typically limited to small spatial scales 11 , 12 . Especially, how people experience nature in everyday lives (e.g., urban greenspace) and how people interact with nature under different contexts (e.g., relaxation, celebration, socialization, or daily routines) are particularly difficult to study in a large spatial scale. Recent breakthroughs in the study of cultural ecosystem services and understanding human-nature interactions have been possible through the use of social media. For instance, analyzing the user-defined “tags” of photographs can help understand the context under which the photograph was taken and potentially the self-reported emotional state of users. Analysis of social media photographs has been used, for instance, to study recreational 13 and aesthetic qualities of natural areas 14 , preferences for nature-based activities in protected areas 15 , and associations between the use of outdoor space and happiness 16 . Despite these advances, global multi-country comparisons of cultural ecosystem services are lacking. Coupling social media with artificial intelligence for automated approaches in image recognition opens up unique opportunities to carry out large-scale studies of cultural ecosystem services to advance our understanding in human-nature relationships 17 .

One discipline that has studied the relationships between the experience of nature and human wellbeing is environmental psychology. According to the biophilia hypothesis (i.e., humanity’s innate tendency to connect with nature), humans largely relied on natural resources for survival and reproduction in human history, leading humans to evolve a tendency to prefer being close to nature through an emotional connection 18 . Psychological studies have demonstrated the capacity of nature to increase life satisfaction and improve attention restoration and stress recovery 19 , 20 , 21 . The psychological benefits gained from experiencing nature provide an important aspect of cultural ecosystem services 5 . People’s favorite places tend to have high restorative potential 10 . The locations where individuals can feel relaxed, forget their worries, and reflect on personal matters are often natural spaces 10 . We hypothesize that nature may play a role as a backdrop for key social contexts in a human’s life.

To test this hypothesis, we integrate both the fields of ecosystem services and environmental psychology to study how humans experience nature in various contexts, and how this relates to life satisfaction scores at a national level. Using the concept of cultural ecosystem services, we aim to analyze the links between nature (background), cultural practices (various contexts and activities), and benefits (cultural association between nature and positive social contexts and further life satisfaction). Based on the biophilia hypothesis and the capacity of nature for psychological restoration 19 , 20 , 21 , we hypothesize that humans tend to associate nature with positive social contexts, such as fun activities, celebrations, weddings, honeymoons, and vacations. In addition, we also investigate whether the relationship between nature experience and life satisfaction holds true at a cross-cultural level. We hypothesize that a nation with a stronger culture of experiencing nature would show higher life satisfaction as compared to other nations with a weaker culture in nature experience. We do this at an unprecedented global scale by leveraging on social media data and image recognition using machine learning algorithms.

We analyzed a total of 31,534 social media photographs uploaded on Flickr—a popular social media platform—using the Google Cloud Vision API. We used Flickr as the source of data because there are a large number of users (over 70 million users) and geotagged photographs (over 197 million) 13 . Flickr contains information about the location where many of the uploaded photographs were taken. These geotagged photographs allowed us to identify which country photographs were taken in. The photographs used in this study were geo-located across 185 countries, over a period of 11 years. We first assessed nature labels (i.e., image contents detected and generated by Google Cloud Vision API as nature-related labels) in photographs tagged by the users as “nature” and later checked the frequency of those labels within photographs tagged with specific contexts by users: people’s daily routines (as a baseline for comparisons with other contexts), fun activities, weddings, celebrations, honeymoons, and vacations. These social contexts were selected as they are likely to reflect people’s choice of favorite places when holding memorable social events/activities in their lives.

Analyzing the content of 5,362 photographs tagged by users as “nature”, we listed the most common nature labels identified by the image content analysis. These common nature labels covered from 7.3% to 40.2% of photographs (Fig.  1 ). These labels were subsequently categorized as: water, terrestrial landscapes, plants, animals, and nature in general terms (Fig.  1 ).

Word cloud showing the 40 most common nature labels detected by the image content analysis in 5,362 nature-tagged photographs. Word size is proportional to the frequency of occurrence. Nature labels were subsequently categorized into five different nature categories (color-coded, green: plants, brown: terrestrial landscapes, black: general terms, blue: water, purple: animals).

Comparing the frequencies of these nature labels identified in photographs tagged with various contexts by users (n = 26,172 photographs), we found that, across all five nature categories, photographs tagged with fun activities, honeymoons, and vacations were more likely to have nature labels identified in them than photographs tagged with daily routines (Figs.  2 , 3 , Table  S1 ). Honeymoon and vacation photographs were more likely to have nature labels in them than fun activity photographs, with the exception of animals (Table  S1 ). However, there was no difference between honeymoon photographs and vacation photographs in terms of the frequency of nature labels identified (Table  S1 ). Celebration photographs were less likely to have nature labels than daily routine photographs, except for plants (Figs.  2 , 3 , Table  S1 ). There was generally no significant difference between wedding photographs and daily routine photographs, except that wedding photographs were likely to have more plants and less animals (Figs.  2 , 3 , Table  S1 ). This indicates that people tend to associate fun activities, honeymoons and vacations with nature, but not celebratory social events.

The relationship between social contexts and the presence of nature. The coefficient estimate (± SE) of the generalized linear mixed-effects models for each social context and nature category. A positive (negative) coefficient indicates a more (less) propensity for photographs to contain nature labels than the control photographs. Control photographs were used as the baseline (photographs tagged with “daily” or “routine”). Fun activity, honeymoon, and vacation photographs were more likely to contain nature labels as compared to daily routine photographs, for all categories of nature (Table  S1 ). Celebration photographs were less likely to have nature labels than daily routine photographs, except for plants (Table  S1 ).

The proportion of photographs with nature labels identified with different nature categories (plants, terrestrial landscapes, general terms, water, animals) for each social context (daily routines, fun activities, weddings, celebrations, honeymoons, and vacations). Each point represents one country, and the size of points is proportional to the total number of photographs, and grey points represent the total number of photographs that are less than 10.

There was a wide variation in terms of how commonly nature appeared in photographs across countries (Table  S2 , Fig.  3 ). Nature commonly appeared in the photographs taken in some countries (e.g., for general nature terms: Iceland, Tanzania, Maldives, New Zealand, and Montenegro), but not in others (e.g., for general nature terms: Russia, Myanmar, China, Czech Republic, and Singapore).

We found that, at a cross-national level, there was a positive association between the national life satisfaction score and the proportion of nature labels (plants) in the fun activity photographs (Fig.  4a , Table  S3 , Coefficient = 4.70 ± 1.29, t value = 3.64, unadjusted p value = 0.0006, FDR adjusted p value = 0.039). However, this relationship was not significant in the vacation photographs (Fig.  4b , Table  S3 , Coefficient = 1.95 ± 1.21, t value = 1.61, unadjusted p value = 0.113, FDR adjusted p value = 0.516), which may have been taken by a higher proportion of overseas tourists. This relationship was also not significant in daily routine photographs (Fig.  4c , Table  S3 , Coefficient = −1.95 ± 3.96, t value = −0.49, unadjusted p value = 0.626, FDR adjusted p value = 0.881). These results suggest that the context-dependent relationship between the national level of life satisfaction score and nature experience appears in the residents of the country.

The relationship between national life satisfaction scores and the proportion of photographs with plant-related labels identified in three social contexts ( a fun activity, b vacation, c daily routine). National life satisfaction was positively associated with the proportion of nature labels (plants) in fun activity photographs, but not associated in the context of vacations and daily routines. The size of the point is proportional to the number of photographs.

Our results reveal that people are more likely to interact with nature in the context of fun activities, honeymoons, and vacations, suggesting an association between nature and these fun or relaxing moments. We also find that countries with more nature (plant-related) in fun activity photographs had higher life satisfaction, such as Costa Rica and Finland. These results, taken together, suggest the importance of nature in providing the background to positive social contexts, presumably fond memories, as well as in contributing to life satisfaction in communities worldwide.

A preference for natural environments during fun activities supports the biophilia hypothesis 18 . This biophilic relationship is more evident in the context of vacations and honeymoons, as both social contexts are intended to provide relaxation from daily routines and the possibly stressful period of organizing weddings or other celebratory events. This implies that humans not only associate nature with emotional happiness but also desire to experience nature probably because of experiences of awe, relaxation, and stress relief  22 , 23 . For instance, visiting nature has been shown to improve cognitive ability, reduce stress, and lower the risk of depression 5 , 19 , 24 . These results further confirm the importance of nature for travel and tourism worldwide 25 , which not only provides economic value but also psychological and cultural values.

Landscape aesthetics as a cultural ecosystem service is particularly important given that the biophilic relationship is pervasive across cultures. Analyzing photographs allows us to understand what and when people want to capture as memories and share with other people. The high frequency of nature in photographs taken during fun activities and vacations implies the significance of nature in some of our fondest memories. For example, national parks in South Africa and marine sites in the UK provide cultural and social values by providing a place identity (a sense of place, such as “reliving childhood memories” and “I miss these sites when I have been away from them for a long time”) 26 , 27 . Similarly, the Satoyama landscape in Japan tends to be regarded as “home” for many Japanese people 28 . Some other famous natural landscapes have been identified as important cultural values to local communities, such as the Waikaraka Estuary in New Zealand 29 and the Arafura-Timor seascape in Southeast Asia 30 . The human influence and loss of nature could potentially lead to the loss of these natural backgrounds to fond memories as well as diminish the cultural values of ecosystem services 30 .

In contrast, wedding photographs were not significantly different from daily routine photographs in terms of the presence of nature labels, and celebration photographs were generally less likely to have nature than daily routine photographs. This suggests that, unlike honeymoons or vacations, urban areas and closed settings (e.g. hotels) are chosen presumably for the convenience to organize social gatherings through high accessibility and to conform to traditional ceremonies 31 , and are thus prioritized over biophilic needs.

People vary in their connectedness to nature 32 , 33 . For example, some people spend time interacting with nature and perceive nature as an important component to their lives, but other people do not. We found that the frequency of nature that appeared in photographs varied widely across countries. This variation could be related to cultural and sociodemographic differences 34 , 35 . For example, it has been shown that Menominee Native Americans spend more time interacting with nature directly in their outdoor activities, as compared to European Americans 34 . Another comparative study also showed that Swiss participants preferred forests with high biodiversity, while Chinese participants did not show such preference 35 . The cultural variation in nature connectedness is important to be considered in the assessment and research in cultural ecosystem services.

Our study further reveals a positive relationship between life satisfaction and the presence of nature in fun activity photographs across multiple countries. Being correlational, these results could either point towards nature contributing to life satisfaction through fun memories, or to the tendency of people satisfied with their lives to spend time in a natural setting. Further research should focus on disentangling the cause and effect behind the observed patterns, as this could be an opportunity to design better programs for interacting with nature and improving human wellbeing. This result also points to the potentially synergistic effect of having social activities in the presence of nature. Different from the other contexts analyzed, fun activities are likely to be a social setting where people tend to interact with each other in a group. The combination of both social interaction and nature connection can be more rewarding than having either element alone 36 , 37 , 38 . Being related to both humans and nature is likely to contribute to our life satisfaction. For instance, it has been shown that in natural environments people tend to behave more altruistically and less selfishly, and that nature enhances social cohesion in communities and increases life satisfaction 23 , 39 . Interactions with nature, or within a natural backdrop, could strengthen social cohesion and improve life satisfaction.

Our analyses present several limitations. Although we know the country where the photograph was taken, we do not know whether it was taken by a local or a foreigner travelling to the country. Also, our focus on English tags assigned by Flickr users biased our results toward English-speaking nations and users. Further research could attempt to replicate our methods across multiple languages and photograph-sharing platforms. Although we performed verification checks to ensure that user-assigned tags led to the intended photographs (e.g. we excluded “proposal” as a tag for a special life event because it turned out to be ambiguous), some tags may lead to unrelated pictures, thus introducing noise to the analysis.

Integrating both the fields of cultural ecosystem services and environmental psychology through a photograph analysis at an unprecedented scale, we showed that people have a preference for nature in their fun activities, vacations, and honeymoons globally. Although our study represents only small steps in this line of inquiry, the findings suggest there is a whole underestimated dimension of the relationship between humans and nature through positive social contexts, presumably in the form of fond memories ultimately associated with life satisfaction. The main implication is that the loss of nature may mean more than losing quantifiable economic and ecological benefits; it could also mean losing the background to our fondest memories.

Choice of tags and nature labels

To select suitable nature elements that people associate with nature, we used “nature” as the tag, which is a self-reported keyword added by social media users when they upload to increase the photographs’ visibility. The common nature-related labels detected and generated by the Google image recognition API within the nature-tagged photographs were used as the nature labels in subsequent analyses.

We considered six contexts in this study. These were daily routines (as the baseline for comparisons), fun activities, weddings, celebrations, honeymoons, and vacations. Similarly, we used “tags” to identify these contexts. Daily routine related tags “daily” and “routine”, on separate searches, were used to retrieve daily routine photographs to be used as the baseline for comparisons. To identify general fun activities, we used the tags “fun” and “activity” on separate searches to retrieve the fun activity photographs. To investigate whether nature labels were more likely to be present in critical life events (weddings and honeymoons), we used wedding-related tags “wedding” and “marriage” to retrieve wedding photographs. The tag “honeymoon” was used solely for the honeymoon photographs. To distinguish between weddings and other types of celebrations as well as between honeymoons and other types of vacations, we also used the tag “celebration” to correspond to the celebration photographs, and vacation-related tags “vacation”, “holiday”, and “travel” to retrieve vacation photographs. Contexts and the tags used are summarized in Table  S4 .

Image extraction and content detection

To extract photographs globally, we used Flickr’s public API to retrieve photographs with tags. We used the abovementioned 12 target tags, and retrieved photographs across 11 years, from 1 st of January 2008 to 31 st December 2018. As users varied in the number of photographs uploaded, we randomly selected one photograph from each Flickr user per returned tag search and therefore each photograph corresponds to an unique user in each tag search. We retrieved only photographs that users of Flickr had chosen to make publicly visible, by filtering the privacy setting. We also extracted all other tags that users added in the retrieved photographs to confirm that the retrieved photographs contained the target tags. Photographs without target tags were removed. To identify the geographical location of the photographs, we also extracted the GPS coordinates of the photographs and used the revgeo package with OpenStreetMap 40 to identify the country of origin (n = 185).

To automatically detect the content within photographs, we used the Google Cloud Vision API through the RoogleVision package in R v3.5.3 41 . We used the label detection function to detect the content in a photograph. The Vision API can detect and generate various labels such as general objects, activities, locations, and products. We extracted a maximum of 15 labels from each photograph with a minimum confidence score of 0.5 (ranging from 0 to 1).

We performed a random manual check of 200 photographs (10 photographs across 20 countries) to verify the tags linked with the intended photographs, locations of photographs, and the accuracy of label detection. Among 200 photographs, all photographs showed correct contexts and countries, and captured nature content correctly for 91% of photographs (182/200) with the use of our nature labels.

Statistical analyses

Association between the presence of natural labels and tags.

We obtained 5,362 nature-tagged photographs. To understand what natural elements people may associate with nature, we first identified the common natural labels in the nature-tagged photographs. The Google Cloud Vision API detected and generated a total number of 2,942 labels, and we selected the 50 most frequently shown labels (each label appeared at least in 389 photographs among nature-tagged photographs). After filtering out irrelevant and ambiguous labels (i.e., adaptation, evening, green, morning, photography, reflection, sky, cloud, atmosphere, and atmospheric phenomenon), we grouped the nature-related labels into five nature categories: water, terrestrial landscapes, plants, animals, and nature in general terms (Table  S5 with frequency). These natural labels were used as the labels to identify the presence of nature in the photographs with various contexts.

Photographs that were retrieved using the “celebration” tag may actually be wedding photographs and, similarly, the “vacation” tag may retrieve honeymoon photographs. To further refine the separation of wedding photographs from generic celebration photographs, we searched “wedding” tags in celebration-tagged photographs, and those photographs were then categorized as wedding photographs. Similarly, we searched “honeymoon” tags among vacation-tagged photographs and considered those photographs as honeymoon photographs. After the regrouping, some photographs that were tagged with multiple target tags (e.g., fun and holiday) were included in the sample of more than one contexts, as they may contain multiple contexts according to our definitions. In total, we obtained 26,172 photographs, and 3,781 of them were categorized into more than one contexts. We had 3,236 photographs classed as daily routine photographs, 8,589 photographs classed as fun activity photographs, 3,098 photographs classed as wedding photographs, 4,227 photographs classed as celebration photographs, 880 photographs classed as honeymoon photographs, and 10,129 photographs classed as vacation photographs. To evaluate the effect of including photographs in multiple contexts on the conclusions, a second analysis was run with the dataset after removing repeated photographs (n = 22,391, Table  S6 ).

We performed generalized linear mixed-effects models with a binomial error structure. The presence or absence of certain nature categories (according to previously identified nature labels) was coded as a response variable (e.g., a photograph in which it was detected the presence of the nature label “tree” was considered as an instance of “plants” in the nature category, Table  S5 ). The context was coded as the fixed effect, and country was considered as the random effect. The random effect for country attempted to account for national-level cultural differences and availability of natural space. The random effect for each country was extracted using the ranef function. We performed a total of four sets of analyses with different contexts as the baseline: 1) comparing fun activities, weddings, celebrations, honeymoons, and vacations against daily routines, 2) comparing weddings, celebrations, honeymoons, and vacations against fun activities, 3) comparing between weddings and celebrations, and 4) comparing honeymoons and vacations. We ran five models (for each nature category separately) in each set of analyses except for the natural category animal in 3) and 4) due to convergence failures. The p values were adjusted for multiple comparisons using the false discovery rate (FDR, with a total of 53 p values).

Association between life satisfaction and presence of natural labels in photographs

To investigate the association between the life satisfaction and proportion of photographs with the presence of nature at a cross-national level, we calculated the proportion of the photographs containing nature labels (for each nature category) in each context (i.e., daily routine, fun activity, wedding, celebration, honeymoon, and vacation) for each country. To ensure that each country is adequately represented, we removed countries that had less than 10 photographs for a given context.

We used life satisfaction in the Cantril Ladder scale (ranging from 0 to 10) with the average of survey responses from each country in 2017 42 , 43 . To control for the income of countries, we used GDP per capita based on purchasing power parities in 2017 42 , 43 , 44 . A total of 69 countries were used in the statistical analysis.

We ran linear regressions with life satisfaction as a response variable, and GDP per capita (to control for the relationship between wealth and life satisfaction), proportion of photographs with nature labels for each nature category, and the interaction between both variables were considered as the explanatory variables. We ran different models for different social contexts and each nature category was run separately. The p values were adjusted for multiple comparisons using the false discovery rate (with a total of 60 p values).

Data availability

All the photographs data can be retrieved using Flickr’s public API, and national life satisfaction and GDP data are available in Our World in Data (see ref. 43 ).

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Acknowledgements

We acknowledge research funds from the National Parks Board and the Ministry of National Development, Singapore.

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These authors contributed equally: Chia-chen Chang and Gwyneth Jia Yi Cheng.

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Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, Singapore

Chia-chen Chang, Gwyneth Jia Yi Cheng, Thi Phuong Le Nghiem & L. Roman Carrasco

Department of Architecture, National University of Singapore, 117566, Singapore, Singapore

Xiao Ping Song

School of Biological Sciences, Centre for Biodiversity and Conservation Sciences, University of Queensland, 4072, Brisbane, Australia

Rachel Rui Ying Oh

ETH Zurich, Singapore-ETH Centre, 1 Create Way, 138602, Singapore, Singapore

Xiao Ping Song & Daniel R. Richards

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L.R.C. and D.R.R. conceptualized the research. C.C., G.J.Y.C., L.R.C. collected data and performed data analysis. C.C., and L.R.C. produced the first draft. All authors revised the manuscript.

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Chang, Cc., Cheng, G.J.Y., Nghiem, T.P.L. et al. Social media, nature, and life satisfaction: global evidence of the biophilia hypothesis. Sci Rep 10 , 4125 (2020). https://doi.org/10.1038/s41598-020-60902-w

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Relationship between Social Media Use and Social Anxiety in College Students: Mediation Effect of Communication Capacity

Fengxia lai.

1 Department of Nursing, The First Affiliated Hospital of Soochow University, Suzhou 215006, China

2 School of Nursing, Medical College of Soochow University, Suzhou 215006, China

Lihong Wang

Jiyin zhang, shengnan shan, associated data.

The data presented in this study are available on reasonable request from the corresponding author. The data are not publicly available due to ethical requirements.

An increasing number of college students are experiencing social anxiety in an era of prevalent social networking. College students’ social anxiety may be related to their social media use. However, this relationship has not been confirmed. This study aimed to investigate the relationships between different types of social media use and social anxiety among college students, and the mediation effects of communication capacity in this context. A large sample of 1740 students from seven colleges in China was analyzed. Bivariate correlation and structural equations analysis showed that passive social media use was positively correlated with social anxiety. Active social media use was negatively correlated with social anxiety. Communication capacity partially mediated the relationship between social media use (passive/active) and social anxiety. Active social media use may reduce social anxiety by positively mediating communication capacity, while improved communication capacity may reduce the contribution of passive use to social anxiety. The differences in the effects of different social media use on social anxiety deserve the attention of educators. Developing communication capacity education around college students may help reduce their social anxiety.

1. Introduction

Social anxiety, also known as “social terror”, refers to the negative anxiety that individuals experience in real or imaginary social interaction situations due to the fear or apprehension of receiving negative evaluations from others [ 1 ]. The prevalence of social anxiety in college students is about 7–33% worldwide [ 2 , 3 , 4 ], while in China, up to 12–14% of college students suffer from high levels of social anxiety [ 5 ]. If social anxiety is not corrected or improved, it may develop into a severe social anxiety disorder and continue to affect students’ academic achievement, career development, and mental health [ 6 ]. Given the burden that social anxiety places on people and society, it is imperative to study the mechanisms through which it occurs, and to develop interventions.

Simultaneously, the use of social media has increased dramatically over the past decade, particularly among young people. Social network sites (SNSs) such as Instagram, Facebook, and Twitter have become indispensable parts of people’s lives. According to statistics, there are 2.23 billion monthly active Facebook users worldwide, and this figure has an annual growth rate of 11% [ 7 ]. In China, the number of internet users had reached 1.011 billion by June 2021, with college students accounting for the highest occupational percentage, at over 23.0%.

Since college students frequently use the internet, their psychological status in social interactions may be influenced by the use of social media. According to research, using SNSs may cause personal social anxiety [ 8 ]. Several theories have shed light on possible mechanisms through which social media use triggers social anxiety. According to the self-presentation theory [ 9 ], individuals may be more sensitive to negative evaluations of others, and even tend to guess that others have negative evaluations of them in their online self-presentation, which causes social anxiety. Individuals use others as a standard of comparison for self-evaluation in the absence of actual reference material, according to classical social comparison theory [ 10 ], especially in the absence of communication, and passive use of SNSs by individuals triggers more upward social comparison. According to behaviorist theory [ 6 ], social anxiety is caused by a conditioned reflex of emotional response, implying that social anxiety may be caused by a lack of social skills and, more precisely, communication capacity.

This evidence calls for a better understanding of the risk factors for social anxiety. These factors also include the way social media is used, interpersonal communication capacity, and previous experiences of social frustration. In this research, we aim to examine the relationship between social media use and social anxiety, taking into consideration the mediating role of communication capacity.

1.1. Social Media Use and Social Anxiety

Social media provides an online medium that allows users to add “friends” to the same network and share their personal feelings, photos, etc., with these “friends” [ 11 ]. The use of social media makes social comparison easier among young adults, leading to poor mental health and life dissatisfaction [ 12 ]. Some studies have found that social media use may trigger social anxiety in individuals. A study conducted in Kolkata discovered that social networking sites (SNSs) and dependence on them had significant associations with anxiety and depression among medical students [ 13 ]. Furthermore, according to a Hong Kong, China study, students who spent more time on SNSs had more severe depression and anxiety problems [ 14 ]. Users of social media may experience a physiological stress response as a result of receiving negative feedback from others, cyberbullying, becoming more aware of stressful events occurring in the lives of others, and feeling pressure to keep social networks updated [ 15 , 16 ]. Social media use may also lead to general communication overload, as individuals are bombarded with messages from multiple electronic channels at the same time, which is linked to psychological distress [ 17 ].

Researchers have categorized social media use into active and passive use based on the different ways social networking sites are used [ 18 ]. Active social media use is actively communicating with others (posting their news, commenting on friends’ posts, and other information-generating behaviors); passive social media use mainly refers to browsing social networking sites without interacting with others (viewing friends’ news and not participating in comments) [ 19 ]. To date, the literature distinguishing the different ways of using social media remains limited. However, a recent study suggests that there may be differences in the impact of different social media uses on individual mental health [ 20 ]. Active and passive Facebook use was also found to show opposing effects on loneliness [ 18 ]. Based on prior evidence, we came up with the following hypothesis:

Higher active social media use and lower passive social media use are positively associated with lower social anxiety.

1.2. The Mediating Role of Communication Capacity

Communication capacity refers to the ability to receive and transmit information, to effectively and clearly express thoughts, feelings, and opinions to others through written, oral, and nonverbal cues, and to quickly and accurately interpret the information transmitted by others to understand their thoughts, feelings, and attitudes [ 21 ]. Multiple lines of evidence suggest that internet use is associated with poorer cognitive-emotional regulation and communication capacity [ 22 , 23 , 24 , 25 ]. Furthermore, according to behaviorist theory, social anxiety is caused by a conditioned reflex of emotional response, implying that social anxiety may be caused by a lack of necessary communication capacity [ 6 ]. Hence, we decided to focus on communication capacity as a mediator of social anxiety.

While social media users may gain a lot of social contact as a result of their internet use, it decreases face-to-face contact, which may impair their social skills in the real world [ 23 ]. Individuals must constantly practice communication skills and modify their behavior in response to feedback from others, and focusing on internet use may limit people’s opportunities to practice communication skills and correct communication capacity deficits [ 23 ]. Heavy use of online social networks, for example, has been shown to reduce individuals’ intimacy and time with parents and family, while increasing conflict with people close to them [ 24 ]. Adolescents who use social media for extended periods were reported to have more severe social skills deficits [ 25 ]. Furthermore, a recent study found that active social media use improves subjective wellbeing, while passive social media use decreases subjective wellbeing [ 26 ]. Thus, rather than social media itself, the consequences of social media use may be related to how social media is used. Based on past evidence, we propose the following hypothesis:

Higher active social media use and lower passive social media use are positively associated with better communication capacity.

The capacity to communicate is also critical in social interaction, as college students must manage relationships with different people, for example, classmates, teachers, romantic partners, and even strangers. Difficulties with effective communication during social development can lead to difficulties in establishing and maintaining friendships, which can result in anxiety, low mood, and depression [ 27 ]. Evidence suggests that social anxiety disorder is significantly associated with greater degrees of communication difficulties [ 25 ]; in other words, social anxiety is linked to a failure to apply social skills and engage in social interactions [ 28 ]. Effective communication skills may affect people’s capacity to cope with worry in their social interactions [ 29 ], and social communication deficits may underpin anxiety disorders in individuals suffering from social anxiety. Spending time with others, on the other hand, can improve social skills, but socially anxious people tend to avoid social interactions [ 14 ]. Moreover, according to an intervention study, improving social skill knowledge has a positive effect on reducing symptoms of depression and anxiety [ 30 ]. Therefore, for the mediation model of this study, in which social media use is directly related to social anxiety and mediated through communication capacity, we hypothesized that:

Better communication capacity is negatively associated with higher social anxiety.

The theoretical moderated mediation model is represented in Figure 1 .

Theoretical mediation model.

2. Materials and Methods

2.1. study design and participants.

A cross-sectional design was implemented. This study was conducted from June to September 2022 with students at seven public colleges in Suzhou, China. A total of 2192 college students were surveyed by online questionnaire, and data from 1740 individuals were eventually included through lie detection questions screening (valid recovery rate = 79.4%). Study procedures involving human participants followed institutional ethical standards. All participants completed the questionnaire anonymously after providing informed consent.

2.2. Measurements

All measures were self-reported, and data were obtained via Sojump.com (a platform providing functions equivalent to those of Amazon Mechanical Turk). The questionnaire link was shared by teachers for students to fill out anonymously and could be submitted only once.

2.2.1. Basic Information

Basic information was collected via a general information questionnaire designed by the researchers according to the content of the study, including students’ information such as age, gender, year of study, place of origin, family structure, economic level, and ethnicity. Finally, based on previous studies measuring risk factors related to social anxiety in college students [ 3 , 4 , 5 ], participants were asked about class leaders’ experiences, frustration experiences in social interactions, their number of friends on SNSs, and childhood left-behind experiences (i.e., those whose parents are migrant workers and those who were children left at home and cared for by relatives).

2.2.2. Active Social Media Use

Active social media use was measured via the Active SNS Use Scale [ 31 ]. It mainly measures individuals’ active use of social media, such as “updating information on their social networking site pages” and “posting photos on their social networking site pages.” This scale consists of 5 items. Each item is rated on a 5-point scale (1 = never to 5 = always), with higher total scores indicating higher levels of active social media use. The Cronbach’s α value for this questionnaire is 0.77.

2.2.3. Passive Social Media Use

Passive social media use was measured via the Passive SNS use Scale [ 32 ], using a Chinese version revised by Liu Qingqi [ 33 ]. It mainly measures individuals’ passive use of social media, such as “reading friends’ status updates” and “viewing photos uploaded by friends”. This scale consists of 4 items. Each item is rated on a 7-point scale (1 = never to 7 = multiple times a day), with higher total scores indicating higher levels of passive social media use. The Cronbach’s α coefficient for this questionnaire is 0.70.

2.2.4. Communication Capacity

Communication capacity was measured via the Communication Capacity Scale developed for college students by a Chinese scholar [ 34 ]. This scale consists of 38 items in 8 dimensions: respect, listening, empathy, emotional sensitivity, comforting others, emotional control, enthusiasm, and verbal expression. Each dimension includes 3–7 items (see Appendix A for all scale items); each item is rated on a 5-point scale (1 = not at all to 5 = fully), with higher total scores indicating higher levels of passive social media use. In this study, the Cronbach’s α value of this questionnaire was 0.89.

2.2.5. Social Anxiety

Social anxiety was measured via the Interaction Anxiety Scale [ 35 ]. It is mainly used to assess the subjective feelings of individuals’ social anxiety experience and is widely used in related studies. The scale consists of 15 questions and is scored on a 5-point scale (1 = not at all to 5 = fully), with higher total scores indicating higher levels of social anxiety. In this study, the Cronbach’s α value of this questionnaire was 0.81.

2.2.6. Personality Traits

The personality of introversion and extraversion was measured via the Chinese version [ 36 ] of the Eysenck Personality Questionnaire [ 37 ]. The scale is suitable for Chinese adults aged 16 years and above, with the advantage of being easy to understand and simple to measure. As social anxiety is mainly influenced by introversion and extroversion personality traits in the Chinese ethnic context [ 38 , 39 ], only the E scale with 12 items was used in this study to analyze the influence of introversion and extroversion personality factors on the results. In this study, the Cronbach’s α value of this questionnaire was 0.83.

2.3. Statistical Analysis

Descriptive statistics were used to describe participants’ sociodemographic characteristics and the main study variables (age, gender, profession, family structure, the experience of class leaders, etc.). Moreover, we conducted tests of normality and homogeneity of variance. One-way analyses of variance (ANOVAs), t -tests, and Pearson’s r correlations were used to test for the unadjusted associations between variables. Because the data obtained are all self-reported by students, this may lead to common method bias (CMBs). Harman’s single-factor test was used to test CMBs. If a factor accounts for more than 50% of the total variance, common method bias has an impact on the findings [ 40 ]. All these analyses were performed using IBM SPSS 26.0. Then, to achieve the main objective of the study, a simple mediation model with 5000 bootstraps was run using IBM SPSS Amos 22.0 As an estimate, a 95% confidence interval (CI) was provided. For the mediation effect, a mediator is significant if the 95% CI of the indicator does not include 0 [ 41 ]. Confirmatory factor analyses (CFA) were performed and then evaluated by Hu and Bentler’s [ 42 ] guide to various fit metrics. The following indicators and thresholds are included: the chi-square/degrees of freedom (χ 2 / df < 3.00), the root mean square error of approximation (RMSEA ≤ 0.08), the goodness-of-fit index (GFI > 0.90), and the comparative fit index (CFI ≥ 0.95). All statistical tests were two-tailed, and the level of significance was set at 0.05.

2.4. Ethical Considerations

The Institutional review board of Soochow University has approved the ethical considerations in research methods and procedures (SUDA20220620H08).

3.1. Descriptive Statistics

The descriptive statistics for all study variables are shown in Table 1 . The mean age of college students was 19 years (standard deviation [SD] = 2), with a range of 15–29 years. 679 (39%) were male students, and 1061 (61%) were female students. The ratios of education levels were 23.0% (undergraduate) and 77.0% (junior college). 57.0% were only children and 43.0% were non-only children; 59.5% had served as student leaders and 40.0% had not. The mean Interaction Anxiety Scale score was (43.46 ± 8.33), indicating a moderate to high level of social anxiety.

Participants’ sociodemographic characteristics (N = 1740).

Notes: SNSs: Social networking sites; EPQ: Eysenck Personality Questionnaire.

There were significant differences in social anxiety among college students by gender, family income, the experience of being left behind in childhood, frustration experiences in social interactions, and personality traits ( Table 1 ). According to hoc tests, college students with a per capita household income of less than RMB 3000 had higher levels of social anxiety than those with a per capita household income of more than RMB 5000. The lower the income, the higher the level of social anxiety. Students with more frequent experiences of interpersonal frustration had higher levels of social anxiety than those with occasional or no such experiences. Students with introverted personality traits had higher social anxiety levels than those with intermediate and extroverted personality traits. There were no significant differences in social anxiety between the other sociodemographic variables.

3.2. Correlations between Social Media Use, Communication Capacity, and Social Anxiety

Pearson’s correlation analysis ( Table 2 ) showed that active social media use was negatively correlated with social anxiety (r = −0.342, p < 0.001) and positively correlated with communication capacity (r = 0.514, p < 0.01). Passive social media use was positively correlated with social anxiety (r = 0.525, p < 0.01) and negatively correlated with communication capacity (r = −0.253, p < 0.01). Communication capacity was negatively associated with social anxiety (r = −0.371, p < 0.01).

Correlation analysis of social media use, communication capacity, and social anxiety (r).

Note: * p < 0.05, ** p < 0.01.

3.3. Common Method Bias Test

Harman’s single-factor test showed that a total of 10 factors’ eigenvalues were >1, the interpretation rate of the first factor was 33.47% (<50%), and there was no serious common method bias.

3.4. Model Test

We used structural equation modeling with observed variables in SPSS Amos 22.0 to test the relationships between social media use, communication capacity, and social anxiety, while controlling for sociodemographic variables (gender, income, experience of being-left-behind, experience of interpersonal frustration, and personality traits). Figure 2 shows a simplified version of the calculated structural equation model. According to Table 3 , the following model can be accepted: χ 2 / df = 2.121, CFI = 0.975, GFI = 0.975, AGFI = 0.998, and RMSEA = 0.039.

Model of the mediating effect of communication capacity on the association between social media use and social anxiety. Note: ** p < 0.01. The normalized path coefficient is represented by the number on the solid line. There are two mediation paths in the diagram: 1. Passive social media use → Communication capacity → Social anxiety 2. Active social media use → Communication capacity → Social anxiety.

Structural equation model fit index.

Note: CMIN/DF, Chi-square minimum degrees of freedom; CFI, comparative fit index; GFI, goodness of fit index; AGFI, adjusted goodness of fit index; RMSEA, root mean square error of approximation.

The results ( Table 4 ) showed that active and passive social media use had a positive and negative predictive effect on communication capacity (β = 1.697 and −0.700, p < 0.01), respectively, explaining 32.8% of the variance in communication capacity. Active and passive social media use negatively predicted social anxiety (β = −0.477 and 0.646, p < 0.01) and explained 41.3% of the variance in social anxiety. Bootstrap repeat sampling was set to 5000 and with 95% CI. The results showed that the direct effects were −0.477 and 0.646; the 95% CIs were (−0.552 to −0.401) ( p < 0.001) and (0.577 to 0.715) ( p < 0.001). In contrast, the indirect effects were −0.100 and 0.050, and the 95% CIs were (−0.086 to −0.030) ( p < 0.001) and (0.007 to 0.059) ( p < 0.001). The study revealed that the direct and indirect effects of social media use on social anxiety are statistically significant; 95% of the CIs did not include zero, which indicated that there was a significant mediation effect of communication capacity on the relationship between social media use and social anxiety.

Bootstrap analysis of the mediating effect of communication ability between social media use and social anxiety.

4. Discussion

This study confirmed that higher active social media use and lower passive social media use were associated with lower social anxiety. Furthermore, this relationship was partially mediated by communication capacity.

4.1. Social Anxiety Was Affected by Gender, Family Income, the Experience of Being Left behind in Childhood, Frustration Experience in Social Interactions, and Personality Traits

Social anxiety problems developed by college students during their development are often the result of a combination of personal, external, and other factors. Research has documented that sociodemographic variables are crucial factors related to social anxiety [ 43 , 44 ]. In this study, female students perceived more social anxiety than male students. According to self-construction theory [ 45 ], men and women have different understandings of self-awareness. Men tend to construct and maintain an independent sense of self in which others are separate from the self. In contrast, women tend to construct an interdependent sense of self in which others are also considered an essential part of the self-construction. This difference in self-awareness may lead female college students to show nervousness and anxiety during social interactions and sensitivity to the evaluation of social partners. They tend to invest time and energy in repeatedly recalling and evaluating their performance after social interactions, and thus are more likely to experience high levels of social anxiety [ 46 ]. Moreover, students’ family economic level was associated with social anxiety. Social anxiety was higher among college students with lower monthly per capita household income, which may be related to low self-esteem due to low purchasing power [ 47 ]. Similarly, a study by Jefferies [ 48 ] on social anxiety among young people in seven countries showed that the unemployed population had higher social anxiety than those employed.

On the other hand, experiences of being left behind in childhood and interpersonal frustration in social interactions were also associated with higher levels of social anxiety. This triggering mechanism may work through the insecure attachment type of the students [ 49 ]. Experiences of traumatic events and adverse life events suffered in childhood have a profound impact on individuals. They become more fearful of interacting with people, fear being judged, have a negative, skeptical attitude toward themselves, and do not participate in as many social activities [ 50 , 51 ]. The findings also showed that extraversion was negatively related to social anxiety; this is consistent with previous studies [ 50 , 52 ]. Highly extroverted people tend to be sociable, talkative, enthusiastic, and confident [ 53 ]. These people are more likely to engage in social activities and feel energized by social interactions [ 54 ]. Hence, they are less likely to report social anxiety.

4.2. Different Manners of Social Media Use Correlated Differently with Social Anxiety and Communication Capacity

Our results showed that the relationship between different use of social media and social anxiety among college students varied. Active social media use was negatively associated with social anxiety, while passive social media use was positively associated with social anxiety. Although the effects were somewhat weak according to the effect size criteria used by Cohen [ 55 ], the results still support our research hypothesis 1. In previous studies, less research has been conducted on active social media use, with most of them pointing to positive psychological outcomes [ 31 , 56 , 57 ]. The reason for this may be related to the fact that active social media use enhances social communication, leading to an increase in daily contact and emotional interaction [ 18 ]. Users increase their positive emotions by interacting directly with other users and increasing their supportive interactions online [ 58 ], thereby reducing social anxiety. Passive social media use, on the other hand, points to adverse outcomes in this study. Previous research has shown that passive use of social media positively predicts loneliness, leads to a decrease in individual wellbeing, and affects adolescent body image worries [ 59 ]. This is because the content presented by individuals on the internet can make the viewers feel that their friends’ life is better than their own, which in turn affects subjective wellbeing [ 60 ].

Moreover, this study revealed that active social media use was positively associated with the communication capacity of college students, while passive use was the opposite. Active online communication has been shown to have a beneficial effect on individuals. For instance, active social media use can indirectly influence friendship quality through positive SNS feedback, and can positively predict friendship quality [ 61 ]. On the other hand, passive social media use may partially replace the function of real-life interactions and also crowd out the real-life interaction time of college students. It may diminish direct, face-to-face interactions between people, causing college students to alienate themselves from real-world interpersonal interactions to a certain extent, and affecting the improvement of interpersonal communication capacity [ 62 ].

4.3. Communication Capacity Was Negatively Correlated with Social Anxiety

According to previous studies, maladaptive behavior and irrational cognitive perceptions are two important causes of social anxiety among college students [ 20 ]. Behaviorism suggests that, as an emotional response, social anxiety stems from conditioned effects and explains the formation of social anxiety through the principle of conditioned effects and social learning theory [ 63 ]. That is, social anxiety can arise from a lack of social skills. The stronger the individual’s communication capacity, the lower the level of social anxiety. A possible explanation for this is that the greater one’s communication capacity is, the more clearly one can communicate one’s wishes and ideas to others, and the more acutely one can detect the subtle emotional feelings of others through their body language. Hence, one tends to build up a good level of confidence when interacting socially with others, which, as a good emotional experience, helps to reduce the occurrence of social anxiety [ 64 ]. On the other hand, people with lower communication capacity may have a poorer sense of communication experience in everyday interpersonal interactions due to their lack of essential communication skills, and are therefore more reluctant to engage in frequent interactions with people. The less they communicate with people, the more they fear communicating with people, thus increasing their level of social anxiety [ 65 ].

4.4. The Mediating Role of Communication Capacity

The other results of this study corroborate the theoretical validity of the mediation model; communication capacity partially mediates the relationship between social media use and social anxiety, which means that communication capacity represents a potential underlying mechanism that could partially explain how social media use is linked with social anxiety. That is, promoting positive social media use and decreasing passive social media use as ways to build up communication capacity might help to relieve social anxiety among college students. Specifically, active use of social media strengthens relational connections between individuals and provides a supportive environment for improving communication capacity [ 66 ], thus helping to reduce social anxiety. On the other hand, passive social media use significantly increases the risk of developing social anxiety, which can be buffered by enhancing communication capacity. Behaviorism also suggests that social anxiety can be generated by a lack of social skills [ 63 ]. Therefore, the development of interventions oriented towards enhancing the communication capacity of college students is crucial today, when social networks are prevalent. This may help these individuals to expand their social communication resources and strengthen their interpersonal support, thereby reducing social anxiety.

This study has a few limitations. First, while this study was limited to public colleges, there were some differences in students’ use of social media across different types of schools. To improve the generalizability of the results, future studies could replicate this study in other educational institutions (e.g., private or international schools). Second, our mediation model is based on a priori, derived from previous studies. However, it is only one of several reasonable and possible models examining how different variables are related. Future research needs to consider the mediating role of other variables not studied in this research, and verify whether the outcomes are replicated at other educational levels. Finally, while the findings of this study support the hypothesized relationships described in the existing literature, additional prospective studies are required to confirm the results.

5. Conclusions

Our research extends the previous results, showing that the relationship between social media use and social anxiety can be explained when incorporating communication capacity as a mediator. Active social media use was significantly and negatively related to social anxiety, whereas passive social networking site use was significantly and positively related to social anxiety. Reducing the use of passive social media among college students and adopting communication capacity-oriented interventions may yield benefits for improving students’ psychological wellbeing; educators should pay sufficient attention to them.

Appendix A. Items of Communication Capacity Scale

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, F.L. and L.T.; methodology, F.L.; software, F.L.; formal analysis, J.Z. and J.C.; investigation, L.W., L.T. and S.S.; data curation, F.L.; writing—original draft preparation, F.L. and L.W.; writing—review and editing, L.T.; project administration, L.T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Soochow University, China (SUDA20220620H08).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Measuring the effect of social media on student academic performance using a social media influence factor model

• Published: 18 July 2022
• Volume 28 , pages 1165–1188, ( 2023 )

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• Mohammed Nurudeen   ORCID: orcid.org/0000-0001-6711-6735 1 ,
• Siddique Abdul-Samad 2 ,
• Emmanuel Owusu-Oware 1 ,
• Godfred Yaw Koi-Akrofi 1 &
• Hannah Ayaba Tanye 1  

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With the advent of smartphones and fourth generation mobile technologies, the effect of social media on society has stirred up some debate and researchers across various disciplines have drawn different conclusions. Social media provides university students with a convenient platform to create and share educational content. However, social media may have an addicting effect that may lead to poor health, poor concentration in class, poor time management and consequently poor academic performance. Using a random sample of 623 students from the University of Professional Studies Accra, Ghana, this paper presents a social media influence factor (SMIF) model for measuring the effect of social media on student academic performance. The proposed model is examined using linear regression analysis and the results show a statistically significant negative relationship between SMIF variables and student grade point average (GPA). The model accounted for 30.7% of the variability in student GPA and it demonstrated a prediction quality of 55.4% given the data collected.

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Data availability

The datasets generated during and analyzed during the study are available in figshare repository https://doi.org/10.6084/m9.figshare.14905089.v1

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Acknowledgements

We wish to thank the respondents who took the time to respond to this survey and the University of Professional Studies Accra, Ghana

This research was conducted using the researchers’ annual research allowance which is funding given by the Government of Ghana to all academic staffs in Ghanaian public universities. The research was therefore conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Also, no funding body played any role in the design of the study, collection, analysis, and interpretation of data and in writing the manuscript.

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Nurudeen, M., Abdul-Samad, S., Owusu-Oware, E. et al. Measuring the effect of social media on student academic performance using a social media influence factor model. Educ Inf Technol 28 , 1165–1188 (2023). https://doi.org/10.1007/s10639-022-11196-0

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DOI : https://doi.org/10.1007/s10639-022-11196-0

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Adolescence

More research questions the “social media hypothesis” of mental health, a new study shows that social media does not lead to anxiety or depression..

Posted August 10, 2023 | Reviewed by Gary Drevitch

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• Many believe that social media causes teens to experience depression and anxiety, despite lacking evidence.
• A new study found that when teenagers used social media more, their mental health did not change over time.
• Mainstream media should devote more coverage to studies like this one.

As I’ve discussed previously , conventional wisdom suggests that using social media promotes poor mental health, especially in teenagers . But there is good reason to question this idea. As more high-quality research becomes available, we can see room for nuance and see that social media is not consistently detrimental to everyone’s well-being.

A critical limitation in many existing studies on this topic is that they are cross-sectional. This means all variables are assessed only once, and at the same time. This isn’t necessarily a bad thing; it just means we don’t know how behavioral changes over time might be associated with changes in emotional variables. Longitudinal research helps us to better understand how change happens by measuring these variables repeatedly over a period of months or even years.

Longitudinal research is especially valuable in this case because some young people may use social media to alleviate distress , so we might observe that increases in depression or anxiety will predict increases in social media use , rather than the reverse. On the other hand, if the social media hypothesis is correct, then as teenagers spend more and more time online, this should be followed by decreased mental health (i.e., greater anxiety/depression). But that’s not what the data reveal.

What Researchers Found

A research team in Norway recently published a study in which they tracked young people aged 10-16, and assessed them every 2 years. Each time, the researchers interviewed participants about their behaviors online (e.g., posting photos, “liking,” or commenting on others' posts), and they conducted clinical assessments of depression and anxiety with standardized psychiatric measures. The researchers found no evidence that increased social media use was followed by elevated anxiety or depression. This means that as these teenagers used more social media, their mental health did not change. These findings directly contradict the idea that social media use leads to poor psychological well-being.

The authors are careful to note that even though social media did not make teenagers feel worse, on average, it also did not make them feel better. So, social media use may not have an overall negative or positive effect for the average teenager. This idea is consistent with what I have argued previously , which is that social media use may have differential effects depending on the user’s initial motivations. When people are motivated to use social media because they find it interesting or rewarding, then it’s likelier to make them happy, whereas when they feel compelled or obligated to use it, then it’s likelier to make them feel worse. Motivations matter more than the technology itself.

The researchers also suggest that perhaps subgroups of teenagers may experience different outcomes following social media use, such as those who are bullied or have low self-esteem . The specific content that people view on social media may also play a role. It is also true that digital technologies change rapidly and we cannot assume that all future forms of social media will operate the same way psychologically. New applications have the potential to be better or worse than what people currently use.

Time Trend Data Are Inconclusive

Those who hold with the “social media hypothesis” of mental health will often point to time trend data as evidence. They argue that because social media use has risen in teenagers over the past 15 years, and that teen depression and anxiety has also risen over the same period of time, then those two trends are likely connected.

But if that were true, we ought to be able to observe this trend happening during teenagers’ lives. The fact is, we do not observe this pattern, and these null findings should make us skeptical about such claims. When researchers track teenagers’ mental health over a span of years, there is no link between their social media use and their experiences of depression or anxiety. In the words of the authors , “ the frequency with which adolescents engage in behaviors like posting, liking, and commenting on others’ posts does not influence their risk for symptoms of depression and anxiety .”

It would be great to see more mainstream media coverage of studies like this, especially considering the widespread belief that if young people are permitted to use social media, their mental health will deteriorate. Perhaps parents of teenagers can take some comfort in the fact that for the average user, there is little risk of this.

Cauberghe, V., Van Wesenbeeck, I., De Jans, S., Hudders, L., & Ponnet, K. (2021). How Adolescents Use Social Media to Cope with Feelings of Loneliness and Anxiety During COVID-19 Lockdown. Cyberpsychology, behavior and social networking , 24 (4), 250–257. https://doi.org/10.1089/cyber.2020.0478

Puukko, K., Hietajärvi, L., Maksniemi, E., Alho, K., & Salmela-Aro, K. (2020). Social Media Use and Depressive Symptoms—A Longitudinal Study from Early to Late Adolescence. International Journal of Environmental Research and Public Health , 17 (16), 5921. MDPI AG. Retrieved from http://dx.doi.org/10.3390/ijerph17165921

Steinsbekk, S., Nesi, J., & Wichstrøm, L. (2023). Social media behaviors and symptoms of anxiety and depression. A four-wave cohort study from age 10–16 years. Computers in Human Behavior , 147 , 107859.

Dylan Selterman, Ph.D., is an Associate Teaching Professor at Johns Hopkins University in the Department of Psychological and Brain Sciences. He teaches courses and conducts research on personality traits, happiness, relationships, morality/ethics, game theory, political psychology, and more.

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