A double-edged sword: Exploring associations between engagement with mental health content by social media influencers and psychological well-being

Introduction

Over the past few years, there has been a rise in people suffering from mental health problems and disorders (World Health Organization, 2022). Especially younger people appear to be vulnerable to this public health concern (Blaikie et al., 2024; Pandya & Lodha, 2022), with anxiety and depression being a leading cause for illness among adolescents, and suicide being the third leading cause of death among young adults globally (World Health Organization, 2024). At the same time, young people are the most pervasive users of social media (Statista, 2024), through which they are confronted with abundant information on mental health topics (for an overview, see Devendorf et al., 2020; McCashin & Murphy, 2023). For example, a recent report found that 93% of young adults in the U.S. report using YouTube and 76% Instagram, highlighting the centrality of social media platforms in their daily lives (Pew Research Center, 2024). One important source for mental health information is social media influencers (Basch et al., 2022; Koinig, 2022; Lind & Wickström, 2024), broadly defined as individuals who have built large audiences on social media and exert influence through the content they share on these platforms (Engel et al., 2024). They are content creators who focus on diverse topics, such as health (Schouten et al., 2018), and reach large audiences, granting them the status of opinion leaders (Hudders et al., 2021). Furthermore, social media influencers foster imagined close relationships with their audience, known as parasocial relationships, enhancing perceptions of reliability and similarity with the audience (e.g., H. Kim, 2021; Shan et al., 2019). These factors give them the power to shape their audience’s health-related cognitions, beliefs, and even behaviors (De Jans et al., 2021; Engel et al., 2024; Vranken et al., 2023).

There is some research suggesting that social media, and particularly mental health content, has a complex relationship with individual well-being (Haltigan et al., 2023; O’Reilly et al., 2019; Pretorius et al., 2022). On the positive side, some studies suggest that mental health content on social media can serve as a tool to improve mental health literacy and support well-being (O’Reilly et al., 2019; Pretorius et al., 2022, Triplett et al., 2022), raise awareness of mental health topics (Hasan et al., 2023), and help normalize discussions about mental health (Pretorius et al., 2022). Conversely, some research points to potential negative impacts of mental health content on social media. Specifically, this content may present mental health conditions in glamorized or romanticized ways (Haltigan et al., 2023) – for example, by portraying them as fashionable or creating memes that trivialize their seriousness (Issaka et al., 2024) – which can increase individuals’ tendency to self-identify with certain disorders or conditions (Avella, 2024). This may be particularly pronounced when viewers encounter mental health content from social media influencers with whom they feel a personal connection (Hasan et al., 2023). Complicating this further, social media influencers are not always mental health professionals, but many are laypeople, increasing the risk of sharing inaccurate or oversimplified mental health information or providing harmful guidance (Fielding, 2021). In fact, experts have already noted a rise in young people self-diagnosing mental health conditions based on information encountered on social media, cautioning that this trend could result in inaccurate self-assessments and misaligned treatment efforts, potentially leading to detrimental effects on mental health and self-perception (Kelly, 2023).

Influencer-driven mental health content on social media, therefore, appears to present a double-edged sword, offering the potential for both positive and negative outcomes on young users. While significant research has examined the broader effects of social media use on young people’s mental health and well-being (e.g., Meier & Reinecke, 2020), the unique influence of influencer-led mental health content remains underexplored. Although initial studies have considered the effects of general mental health content on social media (e.g., Hasan et al., 2023), to our knowledge, no empirical research has yet specifically examined how mental health messaging by social media influencers relates to well-being experiences among Generation Z (i.e., individuals born between 1997–2012), the demographic group most likely to be influenced by this content (e.g., De Castro et al., 2021; Lou & H. K. Kim, 2019). This distinction is critical, as social media influencers tend to cultivate high levels of trust and identification with their audiences (e.g., Brown & Basil, 2010; Weismueller et al., 2020), potentially amplifying the influence of the content they share. This study seeks to address this gap by exploring the relationships between engagement with influencer-driven mental health content and three key outcomes: mental health literacy, self-diagnostic tendencies, and overall life satisfaction. Investigating these outcomes allows for a nuanced understanding of the potentially beneficial and adverse associations between engagement with influencer-driven mental health content and young people’s well-being.

Mental Health Content by Social Media Influencers

Social media have become a convenient and widely accessible tool of obtaining relevant mental health information (Fergie et al., 2015; Pretorius et al., 2022). As of October 2024, for instance, there were already 19.5 million posts under the hashtag #mentalhealth on TikTok (based on authors’ own search), a platform particularly popular among young audiences (Statista, 2023). Mental health content is also prominent on other mainstream social media such as Instagram or YouTube (Devendorf et al., 2020; Koinig, 2022). This content includes discussions of various mental health conditions (e.g., depression, anxiety) and may feature personal experiences, expert information, or general discussions of various mental health topics. It typically addresses symptoms, diagnoses, treatment options, as well as potential coping strategies (Basch et al., 2022; Samuel et al., 2024). Such posts are produced by a diverse group of users, many of whom have achieved influencer status due to their follower count, ranging from mental health professionals (e.g., psychotherapists, psychiatrists) to laypeople sharing personal mental health experiences (Fielding, 2021; Triplett et al., 2022). In some cases, mental health-related content may also take the form of commercial messaging, such as influencers promoting therapy services, mental health apps, or other products on behalf of organizations or businesses. For example, social media influencers have promoted services such as the meditation app Headspace or BetterHelp, an online platform that provides access to mental health counselling (Atkins, 2025). In the context of this study, we do not differentiate between types of social media influencers – whether professionals or laypeople – nor do we distinguish between those who exclusively post about mental health and those who do so occasionally. Instead, we consider any mental health-related content created by any type of social media influencers.

Social media influencers hold considerable influence as role models, a status they achieve largely through the formation of parasocial relationships with their audiences. First conceptualized by Horton and Wohl (1956), parasocial relationships refer to the illusion of a direct, face-to-face relationship with media figures, in which audiences feel a personal and evolving connection (Brown, 2015). Such relationships are formed over time through repeated parasocial interactions with the media personae (Horton & Wohl, 1956), in this context typically involving unidirectional engagements such as watching, liking, or commenting on influencer content. This connection encourages followers to adopt attitudes and behaviors promoted by social media influencers (e.g., Brown, 2015; Li et al., 2023) as they strive to emulate them, a process rooted in Kelman’s (1958) Social Influence Theory, where individuals identify themselves with admired figures to foster a sense of shared identity. As a result, recipients of influencer messaging are more invested in the discussed topics as they identify with the source (Brown & Basil, 2010).

Furthermore, social media influencers are often perceived as highly credible sources of information (e.g., Weismueller et al., 2020), which aligns with the Source Credibility Theory (Hovland & Weiss, 1951; Hovland et al., 1953). This theory posits that persuasiveness of a message is positively influenced by the perceived credibility of the source, which is based on perceived trustworthiness, expertise, and attractiveness (Ohanian, 1990). Parasocial relationships can further amplify this perceived credibility, rendering influencers’ messages particularly persuasive (Munnukka et al., 2019) and increasing the likelihood of followers accepting the presented information, regardless of its accuracy (Mena et al., 2020). Taken together, these mechanisms make social media influencers highly persuasive communicators, shaping health-related attitudes, perceptions, and knowledge of their audiences (e.g., Bonnevie et al., 2020; Fielden & Hoch, 2022), as followers tend to identify with them and remain deeply engaged with the topics they discuss.

Therefore, influencer-driven mental health content may influence users’ perceptions of mental health topics (Avella, 2024). These posts have the potential to educate followers, contributing to increased mental health literacy, awareness, and well-being (O’Reilly et al., 2019; Pretorius et al., 2022; Triplett et al., 2022). By raising awareness and reducing stigma, mental health content shared by social media influencers could normalize open discussions about the topic and encourage help-seeking behaviors (Hasan et al., 2023; Pretorius et al., 2022), either directly or through improved mental health literacy (e.g., Bonabi et al., 2016; Gorczynski et al., 2017). Contemporary definitions of mental health literacy encompass all these diverse effects of mental health content on social media (Kutcher et al., 2016). By regularly engaging with mental health content shared by social media influencers, followers may improve their understanding of mental health terminology, recognize symptoms, and become more informed about treatment options and coping strategies (e.g., Pretorius et al., 2022). Evidence of similar effects has been observed in the context of content related to physical health shared by social media influencers (J. Kim et al., 2024). While some studies have begun to explore these outcomes among young users (e.g., Hasan et al., 2023; O’Reilly et al., 2019), the evidence remains limited for this age group, particularly regarding influencer-driven content. Thus, we hypothesize:

H1: Engagement with mental health content by social media influencers is positively associated with mental health literacy.

Prior research has highlighted the significant role of mental health literacy in fostering the recognition, management, and prevention of mental health conditions (Hunt & Eisenberg, 2010; Reavley et al., 2012). In this vein, increased media literacy, fostered through engagement with mental health content on social media, may have important implications for self-awareness. For instance, since mental health literacy contributes to individuals’ knowledge and awareness of mental health conditions, it also enables them to recognize certain signs and symptoms in themselves more readily (Sukmawati et al., 2023). Furthermore, greater mental health literacy has been linked to more positive attitudes toward mental health conditions and reduced stigma, allowing individuals to reflect more openly on their own potential challenges (Chua et al., 2022; Fleary et al., 2022; Lanfredi et al., 2019). This combination of increased awareness and destigmatization through improved mental health literacy may foster self-diagnostic tendencies (Sukmawati et al., 2023), i.e., the practice of independently identifying and diagnosing one’s own mental health condition or disorder without the involvement or guidance of a mental health professional (Aboueid et al., 2019). While quantitative studies by Reavley et al. (2012) and Lanfredi et al. (2019) have explored these dynamics in young people specifically, both relied on student samples, limiting generalizability. Based on the insights of prior research, we propose the following hypothesis:

H2: Mental health literacy is positively associated with self-diagnostic tendencies.

In recent years, there has been a rise in reports of about young people self-diagnosing mental health conditions based on content encountered on social media (e.g., Caron, 2022; Teitell, 2022). Social media play a significant role in shaping mental health perceptions, sometimes encouraging users to identify with specific mental health conditions (Avella, 2024), particularly as posts often romanticize or glamorize these conditions (Haltigan et al., 2023). As previously described, social media influencers are influential sources of health messaging, partly because recipients tend to identify with them. Research suggests that when mental health issues like anxiety are normalized in social media posts, users who feel a connection with the source may be more likely to self-diagnose with the discussed mental health condition (Hasan et al., 2023; Kaňková et al., 2024). This trend, where young people self-diagnose based on influencer-driven mental health content, along with its potential drawbacks, has been widely explored in journalistic accounts (e.g., Caron, 2022; Fielding, 2021; Kelly, 2023; Teitell, 2022), but empirical research in this area remains limited.

Algorithm-driven platforms like TikTok or Instagram could reinforce these dynamics by tailoring content to individual engagement patterns, creating communities where users with shared interests, such as mental health-related topics, feel understood and connected (Corzine & Roy, 2024). This continuous and self-reinforcing engagement cycle can foster a sense of belonging, self-discovery, and understanding, while providing a space where mental health ideas and self-diagnoses are shaped, evaluated, and normalized (Avella, 2024; Corzine & Roy, 2024; Haltigan et al., 2023; Milton et al., 2023). Given these dynamics and lack of sufficient empirical evidence, we ask:

RQ1: Is there a relationship between engagement with mental health content by social media influencers and self-diagnostic tendencies?

Research suggests that engaging in mental health self-diagnosis without professional support carries certain inherent risks. Hynes (2013) highlights the potential drawbacks of self-diagnosing physical health conditions, emphasizing that it can lead to inaccurate interpretations and critical health details being overlooked, potentially delaying necessary medical interventions. Similarly, Wimbarti et al. (2024) caution against relying on online resources for mental health self-diagnosis, underscoring the risk of drawing inaccurate conclusions. Such misinterpretations can escalate anxiety and unwarranted worry, ultimately impacting both physical and mental well-being (Starcevic, 2017; White & Horvitz, 2009; Wimbarti et al., 2024). Furthermore, Foulkes and Andrews (2023) suggest that inaccurately labeling normal levels of psychological distress as a mental health condition may exacerbate symptoms and lead to further heightened emotional distress. However, previous research in this area has generally not focused on young people, leaving a notable gap in understanding how these dynamics may impact this demographic specifically. Based on these considerations, we hypothesize that self-diagnostic tendencies are negatively associated with individual well-being, conceptualized here as life satisfaction, as is most commonly done in studies investigating well-being in the context of social media use (Marciano et al., 2024), leading to our next hypothesis:

H3: Self-diagnostic tendencies are negatively associated with life satisfaction.

In addition to self-diagnosis, another emerging phenomenon linked to mental health-related social media content is the development of functional tic-like behaviors among young people. These symptoms, while distinct, resemble the tics typically associated with Tourette syndrome. Researchers have observed that the occurrence of functional tic-like behaviors correlate with the prevalence of social media content on Tourette syndrome and other tic disorders, leading them to label the condition with terms like “TikTok tics”. Another commonly reproduced mental health condition in this context is dissociative identity disorder, with content by social media influencers featuring multiple “alters” or identity states gaining popularity and sparking concern about the imitation or over-identification with these portrayals among young audiences (Giedinghagen, 2023). This trend is thought to represent a form of mass sociogenic illness – an outbreak of symptoms without a clear physical cause that spreads within a social group due to shared exposure to influential information sources. In this case, the phenomenon is believed to be triggered by mental health-related content appearing on platforms such as TikTok, YouTube, and Instagram (e.g., Giedinghagen, 2023; Müller-Vahl et al., 2021; Olvera et al., 2021; Shmerling, 2022).

The effects of mental health content on social media thus appear complex, with potential for both positive and negative outcomes. On one hand, this content can increase accessibility to mental health information, raise awareness, reduce stigma, and normalize public discussions around mental health topics. For many users, this accessible information could contribute to greater mental health literacy, helping them recognize symptoms, understand treatment options, and seek appropriate help when needed (O’Reilly et al., 2019; Pretorius et al., 2022; Triplett et al., 2022).

On the other hand, concerns are emerging about the potential adverse effects of engaging with mental health content by social media influencers. These influencers often use relatable, personal stories that make their content accessible and appealing, yet this approach could inadvertently romanticize or glamorize mental health conditions (Haltigan et al., 2023), potentially fostering self-diagnosis, which may not always be accurate or clinically grounded (e.g., Hasan et al., 2023; Kaňková et al., 2024). Furthermore, as described, evidence suggests that the effects of mental health content on social media may extend beyond self-diagnosis, possibly even inducing mental health symptoms (e.g., Müller-Vahl et al., 2021; Olvera et al., 2021; Shmerling, 2022).

Given these mixed outcomes, there is a clear need to better understand the relationship between engagement with influencer-driven mental health content and user well-being. Therefore, we pose the following research question:

RQ2: Is there a relationship between engagement with mental health content by social media influencers and life satisfaction?

Methods

To test our hypotheses and address our RQs, we conducted a cross-sectional survey with a total sample of N = 1,113 participants from the United States of America. Participants were recruited through a professional polling company Cint, using national quotas to ensure age and gender distributions representative of the targeted age group, as well as soft quotas for education. We collected responses from July 30 to August 16, 2024. This study was approved by the Institutional Review Board of the Faculty of Social Sciences, University of Vienna (ID: 20240613_031). This study was part of a larger project measuring several unrelated constructs related to well-being, dating app usage, and social media use. All respondents provided their informed consent to participate in the study.

Sample

Since members of Gen Z are particularly susceptible to influencer-driven communication (De Castro et al., 2021; Lou & H. K. Kim, 2019) and are among the most active social media users (Statista, 2024), we focused specifically on this age group. The age of participants thus ranged from 16 to 27 years old (Gen Z definition based on Dimock, 2019), with M_age = 22.05 (SD_age = 2.89). Regarding education level, 39.35% (n = 438) of participants reported low education level (no high school diploma or high school graduate), 36.57% (n = 407) reported high education (college degree), and 24.08% (n = 268) reported middle education level (some college but no degree). There were slightly more participants, who identified as female (n = 596, 53.55%) than male (n = 517, 46.45%). We excluded n = 4 non-binary participants to maintain statistical power when introducing gender as a control variable.

We also excluded participants (n = 27) who chose not to disclose whether they have a formal mental health diagnosis, as this variable was also controlled for in the analysis. Moreover, only active social media users were recruited for the survey.

To ensure data quality, we excluded speeders, defined as participants who completed the survey in less than one-third of the median completion time of all respondents (n = 11). Furthermore, we included two attention checks within the survey. Participants were asked to indicate their agreement with the following statements: I can count to 10 and My birthday is on February 30. Only extreme responses – Completely agree for the first and Completely disagree for the second – were considered correct. Participants who failed both checks (n = 329) were excluded from the analysis.

Measures

We measured participants’ age: How old are you (in years)?, gender: What is your gender? Female; Male; Other, and level of education: What is your highest educational qualification? Did not graduate from high school; High school graduate; Some college, but no degree (yet); 2-year college degree; 4-year college degree; Postgraduate degree (MA, MBA, MD, JD, PhD, etc.). Apart from these sociodemographic characteristics, we measured several constructs. Unless indicated otherwise, variables were measured using five-point scales. For an overview of the descriptive statistics and correlations between the measured variables, see Table 1 and Table 2.

Table 1. Descriptive Statistics of Measured Variables.

Table 2. Correlations Among Measured Variables.

Our independent variable, life satisfaction (M = 3.6, SD = 0.93, ω = .80), was measured with three items from the Satisfaction with Life Scale (SWLS) by Diener et al. (1985), validated in its three-item form by Kjell and Diener (2021). Participants indicated the extent to which they agreed with the following three statements: In most areas, my life meets my ideal expectations, I am satisfied with my life, and My living conditions are excellent.

Our independent variable, engagement with mental health content by social media influencers (M = 2.84, SD = 1.15, ω = .81) was measured with a self-constructed scale comprising three items, drawing on the common conceptualization of social media engagement, which includes liking, commenting, and sharing content (e.g., Demmers et al., 2020; Lou et al., 2019). Participants rated how often (1 - never; 5 - multiple times a day) during the past month they used social media to: like mental health-related posts by influencers, comment on mental health-related posts by influencers, and share mental health-related posts by influencers on your own profile.

Next, we measured participants’ mental health literacy (M = 3.49, SD = 1, ω = .82) with four knowledge-oriented items adapted from Jung et al. (2016). The items were as follows: A person with bipolar disorder may show a dramatic change in mood, Unexplained physical pain or fatigue can be a sign of depression, Cognitive behavioral therapy can change the way a person thinks and reacts to stress, and When a person stops taking care of their appearance, it may be a sign of depression.

We also measured participants’ self-diagnostic tendencies (M = 3.09, SD = 1.29, ω = .82) using the Self-Identification as Having a Mental Illness Scale (SELF-I), adapted from Schomerus et al. (2019), consisting of three items: Current issues I am facing could be the first signs of a mental illness, I feel that the idea of me having a mental illness is realistic, and I could be the type of person that is likely to have a mental illness.

Lastly, we have also asked participants whether they had ever received a formal mental health diagnosis from a professional (e.g., psychologist or psychiatrist), using response options yes, no, and I prefer not to answer, based on Tse and Haslam (2024).

Data Analysis

The statistical analysis was conducted in R (R Core Team, 2023), using the lavaan package (Rosseel, 2012). The dataset and analysis files are available on OSF: https://osf.io/78mrj. Structural equation modeling (SEM) with latent variables incorporating mediation parameters was used to test our hypotheses and address our research questions. The model showed good fit for the data: CFI = .969; TLI = .959, χ²(79) = 282.656, p < .001; RMSEA = .048, 90% CI [.042, .054]; SRMR = .047.

Results

The SEM model implemented in this study examined the associations between engagement with mental health content by social media influencers, mental health literacy, self-diagnostic tendencies, and life satisfaction, while controlling for the effects of formal mental health diagnosis and gender. These controls were included due to evidence suggesting that females are more likely to self-diagnose based on information found online (Fox & Duggan, 2013) and are also more prone to developing symptoms of mental health conditions following exposure to mental health-related content on social media (Frey et al., 2022). Moreover, females tend to have generally higher mental health literacy (Cotton et al., 2006). Additionally, a prior formal mental health diagnosis could influence self-diagnostic tendencies and literacy, as diagnosed individuals may already possess more knowledge or familiarity with mental health topics, potentially shaping their engagement with social media content on this subject. In our sample, 32.26% of participants (n = 359) reported having received a professional diagnosis of a mental health condition. Figure 1 illustrates the model structure alongside coefficients for the direct paths. An overview of the observed effects for both direct and indirect paths is provided in Table 3.

Figure 1. Model Structure and Direct Paths Coefficients. 

Note. ^*p < .05; ^**p < .01; ^***p < .001.

Table 3. Direct and Indirect Effects in Structural Equation Model.

Direct Effects

In H1, we hypothesized that engagement with mental health content by social media influencers would be positively associated with mental health literacy. Our findings supported this hypothesis, with engagement significantly predicting mental health literacy (b = 0.08, SE = 0.03, p = .020), although the effect was relatively small.

In line with our second hypothesis, we found that mental health literacy was positively linked to increased self-diagnostic tendencies (b = 0.61, SE = 0.05, p < .001). Furthermore, we expected self-diagnostic tendencies to be negatively associated with life satisfaction, which was also confirmed by our results (b = −0.28, SE = 0.04, p < .001).

Next, we were interested in the relationship between engagement with mental health content by social media influencers and self-diagnostic tendencies. Our results indicated that engagement was a significant predictor of increased self-diagnostic tendencies (b = 0.29, SE = 0.04, p < .001).

In regard to relationship between engagement with mental health content by social media influencers and life satisfaction, we found a positive direct relationship between the two variables (b = 0.28, SE = 0.03, p < .001).

Finally, in terms of our control variables, gender was negatively associated with self-diagnostic tendencies (b = −0.17, SE = 0.06, p = .006), suggesting that females are more likely to self-diagnose than males. Gender had no significant effect on mental health literacy or life satisfaction. Formal mental health diagnosis was significantly linked to decreased mental health literacy (b = −0.47, SE = 0.06, p < .001) and self-diagnostic tendencies (b = −0.75, SE = 0.07, p < .001). There was no significant association between this variable and life satisfaction.

Indirect Effects

The SEM model revealed several significant indirect effects. First, engagement with mental health content by social media influencers demonstrated a significant negative indirect effect on life satisfaction through self-diagnostic tendencies (b = −0.08, SE = 0.02, p < .001). Additionally, the pathway from engagement to self-diagnostic tendencies, mediated by mental health literacy, was significant and positive (b = 0.05, SE = 0.02, p = .017). Another significant negative indirect effect emerged from engagement to life satisfaction through both mental health literacy and self-diagnostic tendencies (b = −0.01, SE = 0.01, p = .024). The indirect effect of social media influencers engagement on life satisfaction through mental health literacy alone was not significant (b = 0.01, SE = 0.01, p = .052). Overall, the total effect of social media influencers engagement on life satisfaction, combining both indirect and direct effects, was positive and significant (b = 0.20, SE = 0.03, p < .001).

Discussion

The primary objective of this study was to examine the relationships between engagement with mental health content shared by social media influencers, mental health literacy, self-diagnostic tendencies, and, ultimately, overall life satisfaction among young social media users. To achieve this, we conducted a cross-sectional online survey and explored the relationship using a SEM mediation model. Our findings reveal a nuanced relationship between engagement with influencer-driven mental health content and individual well-being, revealing both beneficial and potentially adverse associations. Notably, our results suggest that self-diagnostic tendencies play a pivotal role in shaping the direction of this association.

On the positive side, our results demonstrate a positive association between engagement with mental health content by social media influencers and mental health literacy, suggesting that young users who frequently engage with this content also possess greater knowledge about mental health topics. This supports previous research highlighting the role of mental health content on social media as an educational tool (e.g., O’Reilly et al., 2019; Pretorius et al., 2022; Triplett et al., 2022), which also raises awareness, reduces stigma by normalizing open discussions about mental health, and promotes help-seeking behaviors (Hasan et al., 2023; Pretorius et al., 2022). Mental health literacy is generally considered a predictor of help-seeking behaviors (e.g., Bonabi et al., 2016; Gorczynski et al., 2017). Our findings extend prior research by providing evidence of this association in the specific context of social media influencers and young social media users.

Furthermore, we found a link between mental health literacy and self-diagnostic tendencies, indicating that young people who are knowledgeable about mental health topics are also more likely to self-diagnose with a mental condition or disorder. This finding aligns with previous research that underscores the critical role of mental health literacy in enhancing individuals’ abilities to recognize, manage, and prevent mental health conditions, while also reducing any associated stigma (Fleary et al., 2022; Hunt & Eisenberg, 2010; Lanfredi et al., 2019; Reavley et al., 2012). This increased knowledge may, in turn, enhance individuals’ self-awareness of mental health symptoms, leading them to reflect on their personal experiences and label patterns of distress as mental health conditions (Sukmawati et al., 2023), as also suggested by our findings. These results indicate that mental health literacy likely serves both an educational and introspective function, encouraging a deeper understanding of mental health topics but also potentially fostering self-diagnostic behaviors. This underscores the importance of considering how mental health information is presented and interpreted on social media platforms, given its dual capacity to inform and shape personal perceptions of mental health.

Interestingly, having a formal mental health diagnosis was significantly associated with both lower mental health literacy and lower self-diagnostic tendencies. This may suggest that individuals who have already received a professional diagnosis might rely less on self-directed learning or informal sources such as social media content to understand their mental health, as they are likely receiving structured information and support through clinical channels. Alternatively, these individuals may experience more uncertainty or stigma surrounding mental health, which could reduce their confidence in recognizing or articulating symptoms, thereby lowering their mental health literacy scores. Notably, no significant association was found between formal diagnosis and life satisfaction, suggesting that a diagnosis alone does not determine well-being outcomes in this context. Future research could explore how different diagnostic experiences (e.g., timing, quality of care, perceived stigma) shape individuals’ engagement with informal mental health information and their broader mental health knowledge.

 Our findings also demonstrate that engagement with mental health posts shared by social media influencers directly relates to heightened self-diagnostic tendencies. This finding aligns with numerous journalistic reports suggesting a trend of young people self-diagnosing based on influencer-driven mental health content (e.g., Fielding, 2021; Caron, 2022; Kelly, 2023; Teitell, 2022), offering empirical support for the relationship between these two variables. Drawing from Social Influence Theory (Kelman, 1958), particularly the concept of identification, this pattern might indicate that young people might identify with the content shared by social media influencers, whom they perceive as role models (Hudders et al., 2021), to foster a sense of shared identity with these admired figures. As Hasan et al. (2023) noted, self-identification with the source of mental health messaging is a significant predictor of self-diagnosis based on social media content. Yet, the specific role of self-identification within the context of influencer-driven mental health content warrants further investigation, especially among young audiences.

Additionally, algorithm-driven platforms popular among this demographic (e.g., Statista, 2023) might further amplify this process by repeatedly exposing users to related content based on their engagement, potentially fostering a sense of belonging, self-discovery and being understood (Avella, 2024; Corzine & Roy, 2024; Haltigan et al., 2023; Milton et al., 2023). On top of these direct relationships, our analysis revealed that the relationship between engagement with influencer-driven mental health content and self-diagnostic tendencies is mediated by mental health literacy, offering a more nuanced understanding of these dynamics. This finding further supports the assumption that while engagement with mental health content shared by social media influencers can enhance users’ knowledge and awareness of mental health topics, it may also inadvertently lead them to apply this information personally, contributing to increased self-diagnostic tendencies. However, it is important to note that, given the nature of our methodology and measures, we cannot determine whether participants’ self-diagnostic tendencies reflect a recognition of genuine mental health symptoms or rather over-identification with content encountered online, potentially driven by repeated algorithmic exposure (e.g., Avella, 2024) or aspirational identification with influencers (e.g., Hasan et al., 2023). Future research should aim to disentangle these possibilities to better understand the implications of increased mental health self-diagnosis in this context.

Furthermore, we investigated how engagement with influencer-driven mental health content relates to users’ overall life satisfaction, with results indicating a complex, ambivalent relationship. On the one hand, we observed a positive direct association, suggesting that users who engage with mental health content from influencers might experience enhanced well-being. This positive association may stem from the sense of close connection and similarity that users feel with social media influencers (Brown & Basil, 2010; H. Kim, 2021; Shan et al., 2019), which may positively impact life satisfaction by fulfilling social needs. Additionally, an indirect path further supported this finding, with mental health literacy as a mediator. This pathway suggests that knowledge gained from influencer-driven content might contribute to life satisfaction by empowering users with relevant mental health information. Such content can provide users with coping strategies, practical advice, and resources (Basch et al., 2022; Pretorius et al., 2022; Samuel et al., 2024), which may help them effectively manage their own mental health-related challenges. Thus, mental health literacy may be a protective factor, contributing to overall well-being.

On the other hand, however, when self-diagnosis served as a mediator, the results revealed an opposing trend – engagement with mental health posts created by social media influencers was associated with heightened inclinations to self-diagnose, as previously discussed, and this inclination further correlated with decreased life satisfaction. When considering the full indirect path with both mental health literacy and self-diagnostic tendencies as mediators, similar results emerged, suggesting a nuanced interplay between the positive informational benefits and the potential self-diagnostic risks associated with engagement in this context.

Ultimately, the total effect of engagement with influencer-driven mental health content on life satisfaction, encompassing both direct and indirect pathways, was positive. Nevertheless, the results of this study suggest that self-diagnostic tendencies might play a key role in determining the direction of this association. We encourage further research to explore these dynamics in greater depth and to establish causal links between these variables, as this would advance our understanding of the nuanced impacts of influencer-driven mental health content on young users’ well-being.

Additionally, in the current study, we did not distinguish between different types of social media influencers (e.g., laypeople vs. mental health professionals, based on the size of the follower base: micro, meso, macro) or the specific types of content they share. Addressing these distinctions in future research could offer valuable insights into the effects of influencer-driven mental health content. For instance, evidence suggests that while some social media influencers share content focused on medical information about mental health conditions and potential treatments or offer practical tips and techniques for coping with various types of psychological distress, others may primarily share personal stories of mental health struggles, diagnoses, and treatment journeys (e.g., Samuel et al., 2024). While the former content type might enhance mental health literacy, thereby offering potentially beneficial outcomes, the latter might promote self-diagnosis due to mechanisms like self-identification (Hasan et al., 2023) and parasocial relationships between viewers and influencers (e.g., Brown & Basil, 2010). Similar parallels could be drawn for different influencer types. For instance, social media influencers who are also mental health professionals might be perceived as more credible than non-experts, similar to findings in nutrition-related research (Jenkins et al., 2020), which could ultimately translate, based on source credibility theory (Hovland & Weiss, 1951; Hovland et al., 1953), to enhanced effectiveness of their messaging.

Our findings carry important implications for both health communication and clinical practice. For mental health promotion efforts involving social media influencers, it is essential to ensure that their content is not only engaging but also accurate and responsible (see Motta et al., 2024), given its potential to shape both beneficial outcomes (e.g., increased mental health literacy) as well as unintended risks (e.g., self-diagnosis). Clinicians working with young people may also benefit from understanding how influencer-driven social media content might inform their clients’ perceptions of mental health, possibly shaping how they interpret and communicate their experiences (see Kaňková et al., 2024).

Limitations

As with all research, this study comes with several limitations. First, although we employed a large-scale quota-based sample, the study is cross-sectional in nature, limiting our ability to draw conclusions about the causality and directionality of the observed relationships. Following the Differential Susceptibility to Media Effects model (Valkenburg & Peter, 2013), engagement with mental health content on social media is likely bidirectional, influencing well-being but also shaped by individual traits and emotional states. This suggests that young people who are predisposed to self-diagnosis may actively seek out mental health information online, potentially reinforcing self-diagnostic behaviors. Future longitudinal research would be beneficial to better understand these dynamics.

Second, while we used validated scales on mental health literacy, life satisfaction and self-diagnostic tendencies, these were self-reports and may be susceptible to social desirability bias. For example, participants may have over- or under-reported their engagement with mental health content shared by social media influencers due to memory limitations or social expectations. Moreover, while we interpret higher SELF-I scale scores (Schomerus et al., 2019) as reflecting self-diagnostic tendencies, the scale may also capture a broader awareness of mental health as a medical construct. Various societal influences beyond social media, such as public health messaging, education, or media coverage, could shape this awareness. Future research should consider disentangling these influences and developing or applying more fine-grained measures to assess whether individuals attribute their perceived symptoms to specific social media content or influencers.

Third, while our study focused on young users, mental health-related content on social media may also impact other age groups. Different age demographics might engage differently with influencer-driven content, which could lead to varying well-being outcomes. We thus encourage future studies to expand research to include broader age ranges.

Finally, our sample consisted primarily of participants without a formal mental health diagnosis. It would be valuable for future research to explore how engagement with influencer-driven mental health content may have differential effects depending on users’ mental health status and history. Moreover, cultural contexts likely play a role, as mental health attitudes and help-seeking behaviors may vary widely across countries. We did not collect data on participants’ ethnicity, which limits our ability to explore potential differences in how individuals from diverse backgrounds engage with influencer content. This is particularly relevant given commentary on the influencer industry being predominantly white-dominated (Wellman, 2022), which may impact identification processes and message reception.

Conclusion

To conclude, our study revealed the complex nature of the relationship between influencer-driven mental health content and the well-being of young social media users, revealing both positive and negative associations. Specifically, while engagement with mental health content by social media influencers was associated with greater mental health literacy, it was also linked to lower life satisfaction through heightened self-diagnostic tendencies. This “double-edged sword” warrants further investigation, particularly into which types of mental health content may offer the most benefits or pose potential risks. Future research should also consider how different types of social media influencers – whether mental health professionals or laypeople – contribute to these varied outcomes.

Conflict of Interest

The authors received no financial support for the research, authorship, and/or publication of this article. Moreover, they have no conflicts of interest to disclose.

The authors hereby confirm that this work is original and that the article is not currently being considered for publication by any other journal.

Use of AI Services

During the preparation of this work, the authors used ChatGPT in order to check the written text for clarity and grammar. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.