Navigating the relationship between social media use and mental health in the digital age

Abstract

This study examines the impact of social media use on mental health among young adults through the lens of social comparison theory. Using a sample of 343 participants, data were collected via an online survey and analyzed with SPSS and SmartPLS. The results indicate that the direction of comparison, frequency of social comparison, nature of media content, and relevance of comparison significantly influence mental health outcomes. However, individual differences and comparison targets did not have significant effects. These findings suggest that social comparison behaviours on social media platforms can amplify negative mental health effects, emphasizing the importance of content type and frequency of engagement. The study extends social comparison theory by highlighting how digital interactions shape emotional well-being, particularly in a developing country context. The results provide practical recommendations for policymakers, mental health professionals, and social media platform designers to promote healthier online engagement. Future research should explore longitudinal effects, cross-platform variations, and cultural differences to provide a more comprehensive understanding of this relationship.

Introduction

The use of social media platforms is on the rise, especially among teens and young adults, with its prevalence continuing to grow [1]. As social media sites become more popular, concerns are increasingly being raised about their impact on our mental well-being. This fast-paced digital world has connected us like never before, but it has also led to some second thoughts about how this constant connectivity affects people, particularly regarding mental health. Since social media’s introduction in 2004, its popularity has surged, but there have also been longstanding concerns about what gets shared on these platforms and how such content influences users [2]. This is a complex issue that has been explored by many researchers, and the findings have often varied, leading to some controversial conclusions. Some studies have linked social media use to higher rates of depression, anxiety, and other mental health disorders [3–5]. The relationship between social media usage and mental health is not straightforward, and it continues to be a topic of intense study and debate. Sadagheyani and Tatari [3], for example, found a significant link between excessive use of hypermedia by students and increased levels of anxiety, depression, and stress among young adults. Similarly, research by Braghieri et al. [4] showed that social media use was negatively associated with overall well-being, such as life satisfaction. This suggests that the more time people spend on these platforms, the less satisfied they might feel with their own lives. On the other hand, some studies have proposed that the relationship between social media use and mental health is more complex and have reported potential positive effects as well [6]. This blend of supportive and critical views on social media was echoed in a study by Whillans and Chen [7], who found mixed results among adolescents. While some young people recognized the opportunities for meaningful connection and self-expression that social media offers, they were also aware that excessive use could harm their self-esteem or lead to unfavorable comparisons with others. Furthermore, Viner et al. [8] suggested that factors such as cyberbullying, sleep disturbances, and physical inactivity might mediate the negative effects of social media, further complicating the narrative around social media and mental health [9]. Given the mixed evidence and the ongoing concern about the impact of social media use, especially on young people, there is a pressing need for more research to understand why these patterns emerge. This understanding is crucial for developing effective interventions and strategies to promote well-being in our increasingly digital world. As we continue to explore how social media influences interactions and relationships, this research can provide valuable insights for policymakers, mental health professionals, and even the designers of social media platforms. In addition, this research does not only contribute to the academic discussion but also emphasizes significant implications and suggestions for further study. This study stands as a very worthy contribution to the literature, which is scant on these constructs or factors.

Literature review

Social media and mental health

Social media has become one of the most common types of communication over digital platforms with billions and billions of people interacting via different means daily. Social media comes with many good things such as connecting people and sharing information. Yet worries are rising about the possible impacts on mental health. Numerous studies have examined this relationship [3, 5–9]. For instance, Viner et al. [8] pointed out in a longitudinal study that variables such as cyberbullying, lack of sleep, and low physical activity moderate the effects of exposure to social media on the mental health and well-being of the youth population in England. O’Reilly et al. [6] delved into how teenagers view media’s impact, on well-being finding that it can have both positive and negative effects. In this regard, Brighieri et al. [4] argued that exposure to cyberbullying relates to depressive symptoms and life dissatisfaction, while Abi-Jaoude et al. [5] found a positive relationship between levels of youth use of smartphones and social media with anxiety levels and depression levels. Still, not all studies yield a purely adverse relationship. Some studies even highlighted the possible mental health benefits of social media. For example, Mano and Rosenberg [9] conducted research and found that using social media for different activities was linked to higher well-being and lower depression levels. The type of social media engagement may also play a role, because passive consumption (e.g., scrolling newsfeeds) seems to relate to negative outcomes, while active use such as commenting and sharing might yield positive effects [8]. This therefore implies that the influence of social media on mental health is convoluted and may change according to the pattern of use and particular experience.

Social comparison theory

One theoretical perspective that has been used to explain the potential negative impact of social media on mental health is the social comparison theory [10, 11]. This theory proposes that individuals evaluate themselves based on comparisons with others, and social media provides a constant stream of opportunities for comparison. The social comparison theory (SCT) is a psychological concept positing that individuals determine their self-worth by comparing themselves to others, and these comparisons play a significant role in shaping one’s emotional and psychological well-being [11]. The theory also implies that individuals are motivated to evaluate their competencies relative to the social context using thumbs-up, thumbs-down, or differentials [12]. Individuals compare themselves to others who are better off or worse off to decrease emotional vulnerability in life choices, career expectations, incidental advertisements, health conditions, and financial conditions. Context, evaluation criteria, and comparison validity all interact in complicated and often unpredictable ways [13]. In the context of social media, SCT suggests that people tend to engage in self-evaluations based on the information they perceive about others online. The constant exposure to curated images, experiences, achievements, and idealized lifestyles of others on social media platforms sets the stage for people to compare themselves. This phenomenon often results in a cycle of self-evaluation that can be detrimental to mental health. For instance, when individuals perceive others as more successful, happier, or more fulfilled, they might experience negative emotions such as depression, anxiety, and lowered self-esteem. Furthermore, comparing oneself to seemingly unrealistic or unattainable standards projected through social media can lead to feelings of inadequacy or dissatisfaction, contributing to the negative effect. Social media users might experience negative emotional consequences due to their comparative evaluations of their own lives against the seemingly idealized lives presented on these platforms. The constant exposure to others’ achievements, body images, and experiences may trigger feelings of envy, loneliness, and discontentment. Furthermore, the pressure to portray an idealized image of one’s life on social media can also cause distress when reality falls short of those standards. Sadagheyani and Tatari’s [3] research highlights that social comparison plays a mediating role between social media usage and negative emotions. Their findings suggest that the more people compare themselves to others on social media, the more they are prone to experiencing negative emotional effects. In view of this, the authors proposed the underlisted constructs from the literature to propose a research model as seen in Fig. 1.

Fig. 1.

Study model Source: Author’s Construct

Direction of comparison

The direction of comparison (whether upward or downward) has a substantial impact on the mental health of social media users. By comparing themselves to those they perceive as better off, upward comparisons can lead to poor self-esteem and low self-worth, particularly when a match is found between reality and a perfect picture of perfection in success or beauty [3, 4]. Downward comparisons, on the contrary, can provide short-term ego boosts; however, they can cause a sense of guilt or shame, particularly when people derive pleasure from the misfortune of others [4]. It has been shown that both varieties of comparison, especially when it becomes an internalized practice, are also associated with adverse emotional consequences, and individuals who are most vulnerable to it are the young people, as they are under the influence of their peers and constantly exposed to curation [5, 6, 8].

H1

Direction of comparison will have a significant influence on individual mental health in the digital age.

Frequency and intensity of social comparison

The rate at which individuals compare themselves with others in social media (frequency) and levels of intense emotions attached to the process of making such comparisons (intensity) influences their mental health in a powerful way. As individuals keep being bombarded with the images of others and their success, their beauty, or their lifestyles, people tend to start doubting their lives. Such comparisons, in case they are regular and also accompanied by intense emotions, may result in the development of anxiety, low self-esteem, and even sadness [3, 6, 8]. To illustrate, a user who frequently visits social networking sites and reads what other people are doing on vacation or what they have accomplished, or how good-looking they are, can get the idea that his or her life is not good enough. This strain in the feelings increases more when individuals take those comparisons very seriously. This continuous exposure adds over time, and eats up their well-being. A study has revealed that high and regular social comparisons are some of the strongest elements to negative mental health outcomes in the digital era [4, 6].

H2

Frequency and intensity of social comparison will have a significant influence on individual mental health in the digital age.

Individual differences and vulnerability

Individuals differ in their response to social media contents. Comparing yourself to others may be very painful to some, and to others, they do not find it difficult to do this. Such variations tend to rely on such individual attributes as self-esteem, history of mental disorders, or emotional strength. Another example is that an individual who feels good about himself/herself or capable of coping with stuff may not be easily discouraged by idealized posts [3, 6]. Conversely, a person who is already prone to worry or lack of confidence may be more easily susceptible to the feeling of the inadequacy. Other scientists, however, state that resilient people are also vulnerable to it when they are exposed to highly curated and unrealistic content on a regular basis [4, 8]. For a comparable reason, personal characteristic attributes may become irrelevant with regard to the manner in which social media sites are framed alongside the content provision algorithm promotes. Thus individual differences are useful but they may not be sufficient to defend users against the emotional impacts of social comparison. Hence;

H3

Individual differences and vulnerability will have a significant influence on individual mental health in the digital age.

Nature of the social media content

Mental health is significantly influenced by the kind of materials people watch on social media. Those materials that present beauty that is not realistic, material or idealized lifestyles may drive pressure and result in a sense of deficiency or misery [3, 4, 6]. In the same vein, there is an opportunity to become anxious, terrified, or depressed due to watching negative content, including hate speech, criticism, or cyber bullying. Conversely well-being can be enhanced by positive and encouraging content. Happy messages, real-life stories, or positive remarks might make people feel more connected and do not feel so insecure. This demonstrates that the affective mood and orientation of the materials that people see online can either worsen the number of damages that social comparisons can bring or mitigate them. Hence, the nature of mental health outcomes is a resultant effect of not only the comparison activity, but also the contents being compared.

H4

Nature of the social media content will have a significant influence on individual mental health in the digital age.

Relevance and salience of comparisons

The relevance and salience of social comparisons to an individual’s self-concept or a specific situation play a critical role in determining their impact on mental health. Studies suggest that the relevance and importance of the comparison domain to an individual’s self-concept can significantly influence the emotional and mental health outcomes of social media users. Sadagheyani and Tatari [3] highlighted that the relevance of a comparison to an individual’s self-concept can significantly shape its effect on mental health. Comparing oneself in domains highly important to an individual, such as appearance, career achievements, or relationships, might lead to more pronounced impacts on mental health outcomes. When individuals engage in comparisons in domains central to their self-identity or personal aspirations, the effect on mental health might be more significant due to the heightened importance and salience of these comparisons in shaping self-concept and well-being. Similarly, Braghieri et al. [4] underlined the importance of relevance and salience of comparisons to an individual’s self-concept. Comparisons to domains of high personal relevance, such as body image, lifestyle, or social status, may trigger stronger emotional reactions due to the close connection of these domains to an individual’s sense of self. Such comparisons can significantly affect individuals’ well-being by impacting their self-worth or identity. Viner et al. [8] analyzed the implications of social media use among young people and the relevance of comparisons in shaping mental health outcomes. The study found that comparisons in domains highly relevant to young individuals were more likely to influence their emotional states and well-being. Additionally, O’Reilly et al. [6] stressed the importance of the salience and relevance of the domains of comparison in influencing mental health outcomes. Comparisons in domains that are crucial to an individual’s self-concept or personal goals could significantly shape mental health by affecting self-esteem, satisfaction, and overall well-being. Furthermore, Abi-Jaoude et al. [5] emphasized the significance of the relevance and salience of comparisons in the context of smartphones and social media use among youth, further highlighting the need to understand how different domains of comparison might influence mental health outcomes. In summary, the relevance and salience of social comparisons in domains that are highly important to an individual’s self-concept or personal aspirations can significantly impact mental health outcomes. The findings from these studies underscore the importance of considering the specific domains of comparison and their potential influence on shaping mental health outcomes among social media users. Following from this, the study hypothesizes that;

H5

Relevance and salience of comparison will have a significant influence on individual mental health in the digital age.

Selected comparison targets

Understanding the impact of social media on mental health involves considering the specific targets individuals choose for their social comparisons. The selected comparison targets, such as close friends, celebrities, or influencers, play a crucial role in influencing emotional and mental health outcomes among social media users. Studies have pointed out that different comparison targets can elicit varying emotional responses and mental health implications. Sadagheyani and Tatari [3] emphasized that comparing oneself to various targets might result in diverse mental health responses. For instance, comparing to close friends or peers on social media platforms may lead to more direct and immediate social comparisons, potentially impacting self-esteem, friendship dynamics, and social connectedness. This type of comparison might have more immediate and tangible implications for individuals’ emotional well-being. Moreover, Braghieri et al. [4] supported the significance of the comparison targets in shaping mental health outcomes. Comparing oneself to celebrities or influencers, who often portray idealized and unattainable lifestyles, might lead to feelings of inadequacy and lower self-esteem due to unrealistic standards set by these comparison targets. Viner et al. [8] delved into the social comparisons made by young people on social media and found that the selection of comparison targets was a critical factor influencing mental health and well-being. The study suggested that the diverse comparison targets people select for evaluating themselves could significantly impact their emotional states. Additionally, O’Reilly et al. [6] stressed that the individuals or groups selected for comparison, whether close friends, celebrities, or influencers, contribute significantly to shaping the impact of social media on mental health. The comparisons to these targets might have distinct emotional implications, influencing individuals’ mental health in various ways. Furthermore, Abi-Jaoude et al. [5] highlighted the potential impact of selecting comparison targets among youth using smartphones and social media, further indicating the need to understand how different comparison targets may influence the mental health outcomes of young individuals. The findings from these studies underscore the importance of considering the diversity in comparison targets and their potential influence on shaping mental health outcomes among social media users. Hence;

H6

Selected comparison target will have a significant influence on individual mental health in the digital age.

Materials and methods

The study followed a cross-sectional design. The study targeted a defined population of young adults of varying ages, focusing on the specific segment that frequently engages with social media platforms and is potentially susceptible [13]. Convenience sampling was employed, which involved selecting participants based on their easy availability and accessibility. Convenience sampling was deemed appropriate for efficiently reaching the target population. This approach ensures that the study captures insights from individuals who frequently engage in online social comparison behaviours. While convenience sampling limits generalizability, it remains a practical method for exploratory research in digital behaviour studies. A sample of 343 was used for the study. The questionnaire used for the study underwent a two-phase pre-test involving expert review and pilot testing. First, three academic experts in digital behavior research reviewed the items for clarity and relevance. Next, a pilot sample of 20 university students completed the survey, and their feedback was used to refine ambiguous items and adjust language for improved comprehension. The survey was distributed online via social media platforms, academic networks, and email invitations to maximize reach. The data collection period lasted three weeks, ensuring a diverse range of responses. Participants provided informed consent before participation, and anonymity was maintained throughout the study. The participants were given clear instructions and information about the study’s purpose and their rights regarding anonymity and confidentiality. It included Likert-scale items (1 = Strongly Disagree to 5 = Strongly Agree) to ensure reliability. Key constructs and items include:

Frequency of Intensity of Social Comparison (FISC): “I often compare myself to others while using social media”. Direction of Comparison (DC): “I often compare myself to those who seem to have more success, happiness, or fulfilment than me on social media”. Mental Health (MH): “I have experienced negative emotions after using social media platforms.” Individual Difference and Vulnerability (INDV): “My level of resilience affects how I cope with the emotional consequences of social comparisons”. Nature of Media Content (NAMC): “I often encounter negative, distressing, or hateful content on social media”. Relevance and Salience of Comparison (RSOC): “I feel that certain comparisons impact my emotional well-being more than others”. Selected Comparison Target (SCT2): “I frequently compare myself to close friends or peers on social media platforms”. The study data was analysed using both SPSS and SmartPLS applications. Descriptive statistics, correlations, and regression analysis were conducted to examine the relationships between social media use and mental health outcomes. The questionnaire was subjected to rigorous pre-testing to ascertain content validity and reliability. The study was conducted in strict adherence to ethical guidelines and principles. Informed consent was obtained from all participants. Table 1 presents the demographic distribution of the respondents.

Table 1.

Demographic distribution of respondents

Demographic category	Subcategory	Frequency	Percentage (%)
Gender	Male	190	55.4
	Female	153	44.6
Age	Below 25 years	269	78.4
	25–34 years	74	21.6
Program	Accounting	4	1.2
	Finance	45	13.1
	Marketing	187	54.5
	Procurement	33	9.6
	BIS	39	11.4
	Financial literacy	9	2.6
	Others	26	7.6
Years of using social media	Below 5 years	87	25.4
	6–10 years	164	47.8
	11–15 years	39	11.4
	16 years and above	53	15.5

The demographic data reveals a predominantly young and balanced gender distribution among the participants, with 55.4% male and 44.6% female. The majority, 78.4%, are under 25 years of age, highlighting a youthful cohort, while 21.6% fall within the 25–34 age bracket. Most participants are enrolled in Marketing (54.5%), followed by smaller percentages in Finance (13.1%), Procurement (9.6%), BIS (11.4%), and other programs. A small fraction is studying Accounting (1.2%) or Financial Literacy (2.6%). In terms of social media usage, nearly half of the participants have been using it for 6–10 years (47.8%), with 25.4% having less than 5 years of experience, and a smaller group with over 16 years (15.5%). This demographic profile suggests a study group that is predominantly young, tech-savvy, and diverse in their academic pursuits.

Data analysis and results

The data were analyzed with SmartPLS, an extensively used statistical tool of SEM. SEM is a flexible and effective analytical tool that helps in the establishment of constructs and in looking at their relationships based on the information one gets from observational data. The normality of data, internal consistency, and model fit through convergent and discriminant validity are checked. In addition, the structural model was measured to test the predictive power and accuracy of the model, including the construct relationships.

Results from PLS-SEM algorithm (measurement model)

No issue of multicollinearity was detected since the values of the Variance Inflation Factor (VIF) were seen to be less than 5 (VIF < 5), which also suggests the no-existence of common method bias [14, 15]. Factor loadings, indicator reliability, Cronbach’s Alpha with composite reliability (CR), average variance extracted (AVE), and discriminant validity using the Fornell-Larcker and Heterotrait-Monotrait Ratio (HTMT) were also examined [15, 16]. The construct or factor loadings in Table 2, which are also found in Fig. 2, measure the strength of relationships between observed indicators and their respective latent constructs. In structural equation modeling, a factor loading of 0.6 or higher is generally considered acceptable, indicating that the indicator reliably represents the underlying construct [15]. In this study, all constructs exhibit loadings above the recommended threshold, confirming the validity and reliability of the measurement model. High construct loadings signify that the selected indicators strongly contribute to the respective latent variables. Four items (DOC2, SCT1, RSOC1 and INDV1) with factor loading below the recommended threshold of 0.6 were removed from the data [14, 17] (Fig. 2). A re-test of the model and re-assessment of the parameters resulted in. Indicator reliability was computed by squaring each of the outer loadings (items). A reliable indicator should have a value greater than the minimum acceptable threshold of 0.4 [18]. This requirement ensures that any items not significantly contributing to the study are excluded (Table 2). The results demonstrated that all reliability values exceeded 0.6 [17, 18].

Table 2.

Construct reliability and Validity

		Convergent validity			Internal consistency		Collinearity
Construct	Indicators	loadings	Indicator reliability loadings2	AVE > 0.5	Composite reliability	Cronbach’s Alpha	VIF
Direction of comparison	DOC1	0.715	0.511	0.660	0.853	0.826	1.423
	DOC3	0.743	0.552				1.568
	DOC4	0.887	0.787				2.712
	DOC5	0.888	0.789				2.708
Frequency intensity of social comparison	FISC1	0.848	0.719	0.687	0.881	0.851	1.771
	FISC3	0.772	0.596				1.918
	FISC4	0.836	0.699				1.889
	FISC5	0.855	0.731				2.371
Individual differences and vulnerability	INDV2	0.823	0.677	0.586	0.704	0.670	1.300
	INDV3	0.652	0.425				1.330
	INDV4	0.810	0.656				1.282
Mental health	MEH1	0.786	0.618	0.655	0.832	0.824	1.625
	MEH2	0.777	0.604				1.670
	MEH3	0.870	0.757				2.113
	MEH4	0.801	0.642				1.716
Nature of the media content	NAMC1	0.783	0.613	0.620	0.849	0.847	1.922
	NAMC2	0.802	0.643				2.053
	NAMC3	0.783	0.613				1.834
	NAMC4	0.765	0.585				1.824
	NAMC5	0.804	0.646				1.978
Relevance and salience of comparison	RSOC2	0.833	0.694	0.681	0.857	0.846	1.759
	RSOC3	0.840	0.706				2.310
	RSOC4	0.812	0.659				1.796
	RSOC5	0.815	0.664				1.843
Selected comparison target	SCT2	0.803	0.645	0.592	0.769	0.770	1.812
	SCT3	0.738	0.545				1.377
	SCT4	0.793	0.629				1.743
	SCT5	0.742	0.551				1.372

Fig. 2.

Construct loadings

Moreover, the Average Variance Extracted (AVE) values exceeding 0.5 (AVE > 0.5) indicate strong convergent validity, ensuring that each construct accounts for a significant proportion of the variance observed in the indicators. The Composite Reliability (CR) values surpassing 0.7 further affirm the internal consistency of the construct. The discriminant validity of the constructs was assessed using the Fornell and Larcker [19] criterion and the Heterotrait-Monotrait ratio (HTMT) [20]. According to the Fornell and Larcker (1981) criterion, the square roots of the AVE (highlighted in bold) were greater than the corresponding correlation values in the same columns (Table 3), inidcating that discriminant validity is established. Additionally, the HTMT values were all below 0.9 [21] (Table 4). The PLS-SEM algorithm also provided insights into the robustness of the structural model, specifically showing the predictive power of the factors (R²) and the model’s predictive quality (Q²). Regarding the model’s predictive power, it was found that the independent constructs accounted for 46.2% of individual mental health variance. Hair et al. [15] also noted that good predictive quality is indicated by a Q² value greater than zero (Q² > 0). Ideally, a perfect model would have a Q² of 1, meaning it perfectly represents reality without any errors. The results of this study indicate a good predictive quality with a Q² greater than 0.342.

Table 3.

Discriminant validity

Constructs	DOC	FISC	INDV	MEH	NAMC	RSOC	SCT
DOC	0.812
FISC	0.550	0.829
INDV	− 0.100	− 0.119	0.766
MEH	0.511	0.506	− 0.050	0.809
NAMC	0.576	0.443	− 0.099	0.601	0.788
RSOC	0.527	0.455	− 0.076	0.519	0.570	0.825
SCT	0.573	0.630	− 0.097	0.512	0.541	0.636	0.770

Table 4.

Heterotrait-Monotrait ratio (HTMT)

Constructs	DOC	FISC	INDV	MEH	NAMC	RSOC	SCT
DOC
FISC	0.652
INDV	0.189	0.172
MEH	0.615	0.573	0.179
NAMC	0.680	0.510	0.174	0.713
RSOC	0.621	0.509	0.143	0.596	0.676
SCT	0.727	0.765	0.168	0.635	0.674	0.769

Results from the bootstrapping procedure (structural model)

The bootstrapping procedure was employed to test the hypotheses of this study, confirming the robustness of the proposed structural model. The table analyzes the impact of various factors on mental health, highlighting both significant and insignificant relationships. The Direction of Comparison (DOC), represented by hypothesis H1, shows a positive and significant relationship with mental health (MEH), with a path coefficient of 0.095 and a P value of 0.036. Similarly, hypothesis H2, which pertains to the Frequency and Intensity of Social Comparison (FISC), has a strong positive and highly significant relationship with MEH, evidenced by a path coefficient of 0.205 and a P value of 0.000. On the other hand, Individual Differences and Vulnerability (INDV), hypothesis H3, demonstrates a weak and non-significant relationship with MEH, with a path coefficient of 0.034 and a P value of 0.506, indicating it does not have a meaningful impact on MEH. The Nature of the Content in Media (NAMC), represented by hypothesis H4, shows a substantial positive and highly significant relationship with MEH, with a path coefficient of 0.347 and a P value of 0.000. Relevance and Salience of Comparison (RSOC), hypothesis H5, also has a positive and significant relationship with MEH, with a path coefficient of 0.150 and a P value of 0.001. Finally, the Selected Comparison Target (SCT), hypothesis H6, shows a small and non-significant relationship with MEH, indicated by a path coefficient of 0.048 and a P value of 0.341. In summary, DOC, FISC, NAMC, and RSOC are significant predictors of MEH, while INDV and SCT do not have a significant impact. Table 5, presents a summary of the path coefficients and Hypotheses of the results.

Table 5.

Path coefficient and hypotheses

Path	Hypotheses	B	T-values	P values	Confidence interval 2.5%	Confidence interval 97.5%	Significance (P < 0.05)
DOC→MEH	H1	0.095	2.101	0.036	0.008	0.185	Yes
FISC→MEH	H2	0.205	5.131	0.000	0.127	0.281	Yes
INDV→MEH	H3	0.034	0.666	0.506	− 0.094	0.124	No
NAMC→MEH	H4	0.347	8.614	0.000	0.269	0.425	Yes
RSOC→MEH	H5	0.150	3.349	0.001	0.061	0.239	Yes
SCT→MEH	H6	0.048	0.953	0.341	− 0.052	0.147	No

Discussion and conclusion

This research set out to examine the elements of social media platforms that impact individuals’ mental health. The study took into account a range of factors, including the direction of comparison, the frequency and intensity of social comparisons, individual differences and vulnerabilities, the nature of media content, and the relevance and salience of comparisons, drawing insights from various existing studies. The study found a strong positive relationship between the direction of comparison (DOC) and mental health outcomes. This supports prior research, like the work of Sadagheyani and Tatari [3], which suggests that upward comparisons, where people assess themselves against those they see as more successful, can lead to feelings of inadequacy and reduced well-being. The findings of this study align with these observations, further reinforcing the idea that upward comparisons on social media can be detrimental to mental health, contributing to emotional distress and lower self-esteem. In contrast, downward comparisons, which involve comparing oneself to those perceived as less well-off, might temporarily boost self-esteem but can also induce feelings of guilt, as noted by Braghieri et al. [4].

The study underlines the importance of frequency and intensity of social comparisons (FISC) as a crucial factor influencing mental health, showing a significant relationship between frequent and intense social comparisons and mental health outcomes. Frequent comparisons expose users to constant evaluations against idealized images, leading to ongoing feelings of inadequacy and low self-worth. When these comparisons are also emotionally intense, marked by strong envy or shame, the psychological impact is even more severe. Together, high frequency and intensity create a cycle of self-doubt and emotional distress, particularly among young users. These findings emphasize that it is not just who the individuals compare themselves to, but how often and how deeply they engage in comparison that shapes mental well-being. This highlights the need for awareness, digital literacy, and platform design that mitigates harmful comparison behavior. The findings support the work of [3, 4].

Unexpectedly, the study found that individual differences and vulnerability showed a weak and non-significant relationship with mental health outcomes. This diverges from some previous [3, 4] research that emphasizes the role of individual differences, such as self-esteem and personality traits, in moderating the impact of social comparisons on mental health. However, the lack of significance in this study suggests that, in the context of social media use, the direct impact of these personal characteristics might not be as pronounced as some research has previously indicated. This adds a layer of complexity to the existing body of knowledge, implying that while individual differences are relevant, they may not fundamentally alter the overall relationship between social media and mental health outcomes as some studies have proposed. In addition, one possible explanation for the insignificance of H3 is that external environmental factors, such as social media platform design and algorithm-driven exposure, may play a greater role in shaping social comparison experiences than personal traits. While individual differences (e.g., personality traits, and self-esteem) influence how people react to comparisons, the constant exposure to idealized content may override these personal tendencies, making platform-driven factors more impactful.

The study confirmed that the nature of social media content plays a major role in influencing mental health. Exposure to idealized images, unrealistic standards, or negative interactions such as cyberbullying was strongly associated with emotional distress, anxiety, and low self-worth. This finding supports earlier research [5, 8], showing that it is not merely the act of using social media, but what users are exposed to that matters most. Content that promotes comparison, perfectionism, or hostility can amplify the emotional burden of social media use, especially among vulnerable users. These results highlight the importance of platform-level content regulation and the promotion of positive, authentic, and supportive online environments to protect mental well-being.

The study also found that the relevance and salience of the comparison to an individual’s self-concept had a positive and significant relationship with mental health outcomes. Comparisons related to important aspects of one’s identity, such as appearance, career, or relationships, were more emotionally impactful than those in less relevant domains. This supports prior findings [3, 4] that emphasize the psychological weight of comparisons in areas closely tied to self-concept. When users internalize these comparisons, they are more likely to experience dissatisfaction, anxiety, and reduced self-worth. This highlights the need to help users develop awareness of how personally meaningful comparisons affect their well-being and to promote self-protective cognitive strategies online.

Finally, the study found that the selected comparison target (SCT) had a non-significant relationship with mental health outcomes. This finding challenges some assumptions in the literature that the choice of comparison targets, such as celebrities or close friends, significantly impacts mental health. For example, Braghieri et al. [4] suggested that comparisons with celebrities, who often display idealized lifestyles, could lead to feelings of inadequacy. The insignificance of H6 could be attributed to the evolving nature of comparison targets on social media. Users frequently shift between comparing themselves to peers, celebrities, or influencers, making it difficult to isolate the effects of a specific target. Moreover, social media algorithms often curate content based on engagement patterns rather than user preferences, exposing individuals to a mix of comparison targets that may neutralize any singular effect.

Theoretical implications

This study makes a substantial contribution to the theoretical understanding of social comparison theory, especially within the realm of social media usage. It reinforces and expands upon existing research by highlighting the importance of the direction, frequency, and intensity of social comparisons, alongside the nature of the content viewed on social media, in influencing mental health outcomes. The findings underscore the continued relevance of social comparison theory in our digital age, particularly on social media platforms where upward comparisons (comparing oneself to those perceived as better off) are quite common. The research strongly supports the theory’s idea that people’s self-esteem and emotional well-being are significantly shaped by these comparisons with others. Interestingly, the study found that who people choose to compare themselves to, and their individual differences, had less of an impact than previously thought. This suggests that the act of comparison itself might be more crucial than we have traditionally believed. The relatively weak influence of individual differences calls into question existing theoretical frameworks that prioritize personality traits and self-esteem as key factors in social comparison processes. This opens up the possibility that we may need to refine these theories to better understand how personality interacts with social media environments or to explore whether other, unexamined factors could be at play. Moreover, the study emphasizes the role of contextual factors, such as how relevant and prominent the comparison domains are in shaping mental health outcomes. This insight points to the need for theoretical models of social comparison to incorporate the significance of the specific areas being compared, particularly in a social media landscape filled with diverse content.

Practical implications

The findings from this study have several practical applications for both individuals and mental health professionals in managing the effects of social media on mental health. Educating social media users about the potential risks associated with frequent and intense social comparisons can be a crucial step in mitigating these negative impacts. Awareness campaigns could be designed to help users recognize when they are falling into harmful comparison habits and offer strategies to encourage healthier online behaviors. The study suggests that social media platforms themselves have a role to play in this effort. They could consider introducing tools and features aimed at reducing the frequency and intensity of negative social comparisons. For instance, platforms might offer options for users to filter out content that could provoke unhealthy comparisons or promote content that presents positive and realistic life experiences. Given the significant influence of the type of social media content on mental health, mental health professionals could also focus on developing personalized interventions. These could be tailored to address specific triggers related to social media use, employing cognitive-behavioral strategies to help individuals reframe negative comparisons and build resilience against the often-unrealistic portrayals found online. Platforms should adjust their recommendation algorithms to diversify content, reducing the emphasis on highly curated, idealized lifestyles that fuel social comparison. Social media apps can integrate pop-up messages alerting users when they spend excessive time engaging with comparison-inducing content. Governments should require platforms to publish transparency reports on how their algorithms influence content visibility, particularly regarding body image and lifestyle portrayal. Educational institutions should integrate digital literacy courses to help students critically assess online content and develop healthy social media habits. By implementing these targeted solutions, social media platforms, policymakers, and mental health professionals can work together to create a healthier digital environment that minimizes the negative effects of social comparison.

Limitations of the research

The cross-sectional design of this study presents a limitation in establishing causal relationships between social comparison behaviours and mental health outcomes. While the study identified significant associations, it remains uncertain whether social comparisons directly influence changes in mental health or if individuals with poorer mental health are more likely to engage in specific types of comparisons. Additionally, the study does not differentiate between various social media platforms, each of which has unique user interfaces, cultures, and content dynamics. This lack of differentiation could oversimplify the complex nature of social media interactions, as the effects of social comparisons on mental health may vary depending on the specific characteristics of each platform. Furthermore, by focusing primarily on individual-level factors such as the frequency, intensity, and direction of comparisons, the study overlooks broader contextual and environmental influences. Important variables like offline social support, socio-cultural environment, and digital literacy were not considered, all of which could significantly impact the relationship between social comparisons and mental health. Moreover, while the study investigates the direct effects of social comparisons, it does not delve deeply into potential mediating or moderating factors that could influence these relationships. Elements such as coping mechanisms, emotional resilience, and the nature of social media content might play critical roles in shaping outcomes but were not thoroughly explored.

Suggestions for future research

To gain a deeper understanding of these dynamics, future research should incorporate longitudinal studies to monitor changes in social comparison behaviours and mental health over time. This method would provide clearer insights into the direction of these relationships and uncover any potential long-term effects of social media usage on mental well-being. Unlike cross-sectional studies, which provide only a snapshot in time, longitudinal research allows for a more precise examination of how social media use influences individuals’ well-being over months or even years. This approach would not only clarify the directionality of these relationships whether social media-induced comparisons contribute to mental health challenges or whether pre-existing mental health conditions make individuals more susceptible to negative social comparisons but also shed light on any delayed or cumulative effects. Given the diverse nature of social media platforms, future studies should also differentiate between them to assess how their distinct features influence social comparison processes and mental health outcomes. Comparative research across platforms such as Instagram, Facebook, TikTok, and LinkedIn could offer more detailed perspectives on the unique risks and benefits each one presents. For example, image-heavy platforms like Instagram, which emphasize aesthetics and lifestyle portrayals, may foster more appearance-related comparisons, whereas professional networking sites like LinkedIn may trigger career-related comparisons. In addition to differentiating between platforms, future research should also aim to enhance the diversity and representativeness of study samples. Many existing studies on social comparison and mental health rely on samples that are limited in terms of age, socioeconomic status, or cultural background, making it difficult to generalize findings to broader populations. Expanding research to include more diverse demographic groups such as adolescents, older adults, individuals from varying socioeconomic backgrounds, and those from different cultural contexts could provide a more accurate picture of how social comparison processes operate across society. Further studies should also investigate potential mediators and moderators that might shape the connection between social comparisons and mental well-being. Exploring factors such as social support, coping mechanisms, or digital literacy as mediating influences could help develop more effective strategies for intervention. By addressing these gaps and considering these factors, future research can provide a more nuanced and holistic understanding of the relationship between social media and mental health. These insights could, in turn, inform the development of more effective interventions, such as educational programs, platform design modifications, or policy changes aimed at reducing the negative consequences of online social comparison.

Author contributions

D.O. contributed to the theoretical framework of the study and conducted the statistical analysis. C.D. reviewed the literature and provided insights into the study’s implications. J.N.O.Y. meticulously proofread the manuscript and contributed to the articulation of the study’s implications. L.Q made significant contributions by offering valuable suggestions for the literature review and study discussion.

Funding

There is no funding for this study.

Data availability

Sharing the study data would be unethical since we didn’t inform the participant that their data would be shared publicly.

Declarations

Ethics approval and consent to participate

The study received approval from the Department of Management Sciences, Ethics Committee Board. It was conducted in strict compliance with the ethical guidelines of the department. These guidelines were meticulously followed to uphold the highest ethical standards, ensuring the well-being, rights, and privacy of all participants. Before participating, all individuals were provided with a detailed informed consent form explaining the study’s purpose, potential risks, and benefits. The consent document also outlined the research objectives, guarantees of anonymity and confidentiality, participant responsibilities, and their right to withdraw from the study at any time.

Consent for publication

Not applicable

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable.