Navigating Social Media: Balancing Connectivity With Media Literacy to Combat Misinformation and Protect Mental Well‐Being

ABSTRACT

Background

The pervasive use of social media has created a complex digital ecosystem where high connectivity coexists with significant challenges, including the rapid spread of misinformation, particularly regarding mental health, and documented negative impacts on psychological well‐being. Platform architectures designed for engagement maximization have been identified as central factors in both issues.

Objective

This paper critically analyzes the interconnected relationships between social media use, misinformation dissemination, and mental health impacts, with particular attention to psychiatric misinformation across diagnostic categories (e.g., depression, anxiety, ADHD). A primary objective is to evaluate the potential of advanced critical digital literacy frameworks to serve as protective mechanisms against these dual threats.

Methods

A systematic search was conducted following PRISMA 2020 guidelines across APA PsycInfo, PubMed, JSTOR, and Google Scholar for literature published between January 2018 and March 2026 (updated from the original 2023 search). The search yielded 2672 records. After removing 624 duplicates, 2048 records underwent title and abstract screening, with 1802 excluded. The remaining 246 full‐text articles were assessed for eligibility, resulting in 86 studies included in the final qualitative synthesis. Inter‐rater reliability was established (Cohen's κ = 0.82). Quality assessment was conducted using the Joanna Briggs Institute Checklist, AXIS, and CASP tools, with findings weighted by methodological quality. A thematic analysis was undertaken to synthesize findings.

Results

The analysis reveals that core architectural features of social media platforms, algorithmic curation and engagement‐based metrics, simultaneously foster environments ripe for misinformation spread and contribute to psychological distress, including anxiety, depression, and harmful social comparison. Psychiatric misinformation specifically (e.g., inaccurate claims about treatment effectiveness, diagnostic criteria, and medication side effects) represents a growing concern, particularly on image‐ and video‐based platforms. The findings indicate that conventional media literacy approaches focused solely on fact‐checking are insufficient. Instead, a critical digital literacy framework encompassing algorithmic awareness, data literacy, and emotional awareness is essential for building user resilience, with evidence from high‐quality systematic reviews supporting this approach.

Conclusions

Navigating the complexities of modern social media requires an integrated approach combining “pedagogies of play” for experiential skill development with advocacy for structural change (e.g., algorithmic transparency, well being by design principles). This dual strategy empowers individual users to critically engage with digital content while advocating for ethical platform design, thereby safeguarding both mental well‐being and democratic discourse. Implications for educators, mental health professionals (including competencies for addressing patient encounters with psychiatric misinformation), policymakers, and platform designers are discussed.

1. Introduction

Social media has fundamentally reshaped communication, empowering marginalized voices and enabling global movements like #BlackLivesMatter and #MeToo to challenge entrenched power structures [1, 2]. However, this democratizing potential is shadowed by profound societal risks. The underlying economic model, often termed “surveillance capitalism” [3], creates platforms where algorithmic curation and engagement metrics prioritize profit over user well‐being. This architecture has been shown to amplify emotionally charged and misleading content, fueling the rapid spread of misinformation [4] while research suggests it may foster environments detrimental to mental health, particularly through social comparison and algorithmic echo chambers [5, 6, 7, 8].

A growing body of literature now specifically addresses mental health misinformation, defined as false or misleading information about psychiatric conditions, their causes, treatments, and prognosis [9]. This is distinct from general misinformation in that it directly influences help seeking behaviour, treatment adherence, and stigmatization of individuals with mental illness [10]. Hudon et al. [11] documented that mental health misinformation on platforms like TikTok frequently involves misleading claims about medication side effects, misattribution of normal experiences to psychiatric disorders, and promotion of unproven “natural” treatments. Starvaggi et al. [9] identified that such content often exploits cognitive biases, including confirmation bias and the illusion of explanatory depth, making it particularly resistant to correction.

While concerns about social media's harms are well‐documented, evidence also indicates that effects vary substantially across individuals, contexts, and usage patterns [12, 13]. For some users, particularly those from marginalized communities, social media provides vital social support and identity affirmation [14]. Contemporary evidence supports a more complex, interactional model in which platform architecture interacts with individual characteristics and environmental factors to produce variable outcomes. The relationship between social media use, misinformation exposure, and mental health outcomes is moderated by individual vulnerability factors, including pre‐existing psychiatric symptoms, personality traits, and attachment styles [15, 16]. Digital behaviours, such as passive versus active use, frequency of engagement with health content, and platform choice, significantly influence outcomes [16]. Social context, including offline social support networks and socioeconomic factors, further moderates these relationships. This variability underscores the need to move beyond simplistic narratives of harm or benefit toward a more nuanced understanding of how platform design interacts with user characteristics. Recent meta‐analyses confirm that while social media use is associated with increased anxiety and depression, the effect sizes are small to moderate and highly dependent on usage patterns and individual vulnerabilities [15, 16].

Despite these interlinked challenges, research remains largely siloed into distinct domains: political engagement, misinformation, and psychology, proceeding with limited cross‐disciplinary dialogue. This fragmented approach presents a critical gap. Furthermore, while media literacy is a common prescription, current frameworks are often inadequate; they treat literacy as a static skill for content evaluation while neglecting the dynamic algorithmic forces that actively shape user experience and emotional response [17].

We distinguish Critical Digital Literacy from existing health literacy frameworks. While digital health literacy, as conceptualized by Norman and Skinner [18] and Sørensen et al. [19], emphasizes the ability to access, understand, appraise, and apply health information from digital sources, this framework focuses primarily on information‐seeking and content evaluation. It does not explicitly address the algorithmic and economic forces that shape which content reaches users and how platforms influence emotional responses. Similarly, Nutbeam's [20] health literacy model, while influential, was developed in the pre‐social media era and does not account for the dynamic, participatory nature of digital platforms where users are both consumers and producers of health information. Critical Digital Literacy extends these frameworks by incorporating algorithmic awareness, understanding how recommendation systems curate content, data literacy, recognizing how personal information is monetized, and emotional literacy, identifying how platform design exploits psychological vulnerabilities. This tripartite framework addresses gaps in existing models and provides specific competencies for navigating the contemporary digital environment.

This paper addresses the following research question: How can advanced critical digital literacy frameworks be integrated to simultaneously mitigate the dual challenges of misinformation, particularly psychiatric misinformation, and mental well‐being risks exacerbated by social media platform architectures? Drawing on a systematic narrative synthesis of existing research, we argue that combating misinformation and protecting mental health are not separate battles but two fronts in the same campaign, both requiring a sophisticated understanding of the platform economy.

We ground our analysis in Surveillance Capitalism [3], which illuminates the economic incentives driving engagement‐maximizing algorithms, the root cause of both misinformation proliferation and psychological harm. This is complemented by Critical Digital Literacy [17], which provides the framework for user‐level intervention. Together, these theories address both structural and individual dimensions of the problem.

This paper defines “critical digital literacy” as the capacity to not only evaluate content credibility but also interrogate the algorithmic, economic, and power structures shaping online experiences [17]. We distinguish this from traditional media literacy by its emphasis on understanding platform logics and the emotional design of interfaces. The conceptual novelty of this framework lies in its integration of three interconnected competencies that existing health literacy models do not address: algorithmic awareness (understanding recommendation systems), data literacy (recognizing data monetization), and emotional literacy (identifying psychological vulnerabilities exploited by platform design). This paper synthesizes disconnected research strands to propose a conceptual model that bridges individual competency development through innovative “pedagogies of play” that build algorithmic awareness, with structural advocacy for ethical design. These proposals are presented as promising frameworks requiring empirical evaluation rather than established interventions. Through this integrated roadmap, we aim to equip educators, policymakers, mental health professionals, and designers with the insights needed to foster a more resilient, humane, and ethical digital ecosystem.

The paper proceeds as follows. First, we present our interdisciplinary theoretical framework. Second, we describe our methodology for synthesizing the literature. Third, we present findings on connectivity, misinformation (including psychiatric misinformation specifically), mental health impacts, and media literacy. Fourth, we discuss the implications of our findings and propose a conceptual model for intervention. Finally, we offer recommendations for educators, mental health professionals (including competencies for addressing patient encounters with psychiatric misinformation), clinicians, policymakers, and platform designers.

2. Theoretical Framework

This study is grounded in an interdisciplinary theoretical framework that captures the multifaceted nature of social media's influence. This study defines critical digital literacy as the capacity to evaluate content credibility while also interrogating the algorithmic, economic, and power structures that shape online experiences [17]. Unlike traditional media literacy, which focuses on fact‐checking, critical digital literacy emphasizes understanding platform logics and the emotional design of interfaces.

Table 1 summarizes the key theories informing this analysis.

TABLE 1.

Interdisciplinary theoretical framework.

Theory	Key scholars	Core concepts	Relevance to this study
Connectivism	Siemens [21]	Knowledge creation as networked process; learning emerges through connections between information nodes	Explains how information spreads peer‐to‐peer across platforms
Surveillance Capitalism	Zuboff [3]	Platform monetization of user data; behavioural modification for profit	Reveals economic incentives behind engagement‐maximizing algorithms
Networked Publics/Counterpublics	Jackson et al. [1];	Marginalized groups create alternative spaces for representation and resistance	Explains both empowerment and vulnerability of minoritized users
Participatory Politics	Jenkins et al. [22]	Shift from hierarchical to peer‐driven civic engagement	Illuminates new forms of activism and their limitations
Crisis Informatics	Starbird and Palen [23]	Role of social media in grassroots coordination during emergencies	Highlights both rapid response capabilities and misinformation risks
Attention Economy	Citron and Pasquale [24];	Algorithmic prioritization of emotionally charged content	Links platform design to polarization and mental health impacts
Mental Health Misinformation Frameworks	Starvaggi et al. [9]; Hudon et al. [11]; Lorenzo‐Luaces and Starvaggi [10]	Psychiatric misinformation patterns; cognitive vulnerabilities exploited (confirmation bias, illusion of explanatory depth); clinical competencies for addressing misinformation in practice	Explains how mental health misinformation spreads, its unique characteristics (e.g., pathologizing normal experiences), and implications for clinical practice and help‐seeking behaviour
Critical Digital Literacy	Pangrazio [17]	Beyond fact‐checking to interrogate power structures and algorithmic logics	Provides framework for user‐level intervention

These theories were selected for their complementary explanatory power. Connectivism explains how information propagates through peer networks. Surveillance Capitalism reveals the economic incentives driving platform design. Networked Publics explains how marginalized communities use these platforms for empowerment. Participatory Politics illuminates new forms of civic engagement. Crisis Informatics highlights the dual potential and risk of rapid information sharing. The Attention Economy explains the psychological impact of algorithmic curation. Mental Health Misinformation Frameworks explain the specific patterns and vulnerabilities associated with psychiatric misinformation, including the exploitation of cognitive biases such as confirmation bias and the illusion of explanatory depth [9], as well as the clinical competencies required to address these challenges [10]. Critical Digital Literacy provides the user‐level intervention framework.

The integration of these theories, particularly the addition of Mental Health Misinformation Frameworks, addresses a key gap in previous research, which has often treated general misinformation and psychiatric misinformation as interchangeable. This distinction is important because psychiatric misinformation has unique characteristics: it directly affects help‐seeking behaviour, treatment adherence, and stigmatization of individuals with mental illness [10]. Unlike political or vaccine misinformation, psychiatric misinformation often involves pathologizing normal experiences, misrepresenting treatment options, and exploiting the vulnerability of individuals seeking mental health information [11].

Together, these theoretical perspectives offer a comprehensive lens through which to assess both the empowering potentials and inherent challenges of social media in contemporary society. This interdisciplinary approach allows us to examine the problem at multiple levels, from individual cognition (Critical Digital Literacy, Mental Health Misinformation Frameworks) to platform architecture (Surveillance Capitalism, Attention Economy) to social movements and civic participation (Networked Publics, Participatory Politics), providing a holistic understanding that informs both individual‐level interventions and structural recommendations.

3. Methodology

This paper presents a systematic narrative synthesis of the literature on social media, misinformation, and mental health. A systematic search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) 2020 guidelines to ensure transparency, and the synthesis is thematic rather than meta‐analytic.

3.1. Search Strategy

A comprehensive search was conducted initially in March 2023 and updated in January 2026 across four academic databases: APA PsycInfo, PubMed, JSTOR, and Google Scholar. The search targeted literature published between January 2018 and January 2026, capturing the most recent decade of research following major platform developments and the identification of widespread misinformation concerns. Forward citation searching was conducted for key articles to identify 2024–2026 publications.

Search terms combined Boolean operators across four concept blocks, as detailed in Table 2.

TABLE 2.

Search terms by concept block.

Concept block	Search terms
Platform	Social media OR digital platforms OR social networking sites OR Facebook OR Twitter OR Instagram OR TikTok
Misinformation	Misinformation OR disinformation OR fake news OR false information OR mental health misinformation OR psychiatric misinformation
Mental Health	Mental health OR well‐being OR anxiety OR depression OR psychological distress
Literacy	Media literacy OR digital literacy OR algorithmic literacy OR critical literacy

For Google Scholar, searches were conducted using the same Boolean operators. Given the platform's algorithmic variability, the first 200 results per search string were screened for relevance, and the search was conducted in a private browsing session to minimize personalization effects. All other databases used structured search syntax with screenshots retained for audit.

3.2. Inclusion and Exclusion Criteria

Inclusion criteria:

• Peer‐reviewed journal articles, empirical studies, systematic reviews, and seminal theoretical texts

• Published in English between 2018 and 2026 (with forward citation searching for 2024–2026 publications)

• Directly addressed at least two of the three core themes: social media platforms, misinformation, and mental health impacts

• Examined media/digital literacy as a potential intervention or protective factor

• Studies specifically addressing mental health/psychiatric misinformation.

Exclusion criteria:

• Non‐peer‐reviewed sources (blog posts, opinion pieces, news articles)

• Studies focused solely on one theme without addressing intersections

• Literature older than 5 years unless foundational theoretical work.

3.3. Study Selection Process and Reliability

The study selection process followed PRISMA 2020 guidelines and was conducted by two independent reviewers. A random sample of 20% of records was double‐screened at title and abstract level to establish inter‐rater reliability, which achieved substantial agreement (Cohen's κ = 0.82). Disagreements were resolved through documented discussion; where consensus could not be reached, a third reviewer adjudicated.

3.4. Quality Assessment

The methodological quality of included studies was assessed using appropriate tools: the Joanna Briggs Institute Checklist for systematic reviews, the AXIS tool for cross‐sectional studies, and the CASP checklist for qualitative studies. Quality ratings influenced the synthesis in three ways. First, higher‐quality studies (rated as high or moderate) were weighted more heavily in drawing conclusions, particularly where findings from high‐ and low‐quality studies diverged. Second, methodological limitations identified in lower‐quality studies were explicitly noted when reporting findings, with conclusions tempered accordingly. Third, sensitivity analyses were conducted to assess whether excluding low‐quality studies would substantially alter the synthesis findings; where differences emerged, this is explicitly noted in the results. Studies were not excluded based on quality but were critically appraised in the synthesis, with limitations noted where relevant.

3.5. PRISMA Flow Diagram

The study selection process is summarized in Figure 1.

FIGURE 1.

PRISMA flow diagram of the study selection process. PRISMA 2020 flow diagram depicting the systematic review selection process for studies on social media, misinformation, and mental health (2018–2023). The updated search (January 2026) identified additional records focusing on mental health misinformation and TikTok.

3.6. Data Analysis

A thematic analysis approach was used, following the six‐phase framework of [25]. Selected literature was coded and analysed using NVivo 14 software to identify recurring themes, conflicts, and gaps, with particular focus on studies explicitly connecting platform design, user cognition, and psychological outcomes.

The analysis proceeded through six phases:

Phase 1‐ Familiarization: All included studies were read in full by the lead author, with key points and initial observations recorded.

Phase 2‐ Initial Coding: Descriptive codes were generated inductively through an open coding process. Initial descriptive codes (e.g., “algorithmic amplification”, “social comparison”, “fact‐checking limitations”, “psychiatric misinformation patterns”, “cognitive biases exploited”, “clinical competencies”) were generated, with codes refined iteratively as analysis progressed.

Phase 3‐ Theme Development: Codes were subsequently organized into broader analytical themes through constant comparison. Candidate themes were developed iteratively as coding progressed.

Phase 4‐ Review and Refinement: Themes were reviewed against coded extracts and the full data set to ensure coherence and representativeness.

Phase 5‐ Definition and Naming: Final themes were defined and refined, with clear boundaries established between themes.

Phase 6‐ Writing: Thematic analysis was reported in the results section.

3.6.1. Inter‐Coder Reliability and Disagreement Resolution

Two coders independently coded 20% of included studies. Inter‐coder agreement was established at 87%. Disagreements (13%) were resolved through documented discussion, referencing coded extracts and revisiting the coding framework. Where consensus could not be reached, a third reviewer adjudicated. Disagreements most commonly concerned whether codes should be assigned to the “algorithmic awareness” or “emotional awareness” theme; these were resolved by creating explicit code definitions and examples.

3.6.2. Saturation

Thematic saturation was considered achieved when review of additional studies yielded no new codes or themes. After coding 45 studies, no new themes emerged; though iterative refinement continued with the full data set.

3.7. Study Characteristics and Quality Summary

A summary of the 86 included studies is presented in Table 3. The majority (67%) were empirical studies, with 21% systematic reviews and 12% theoretical works. Quality assessment using the Joanna Briggs Institute Checklist, AXIS, and CASP tools indicated that: 42 studies (49%) were rated as high quality, 31 studies (36%) were rated as moderate quality, and 13 studies (15%) were rated as low quality. Common methodological limitations included small sample sizes (n < 100), cross‐sectional designs (precluding causal inference), and reliance on self‐report measures. These limitations are noted where relevant in the synthesis. A brief summary table categorizing included studies by study design, mental health outcomes, misinformation topic, and social media platform is provided in Table 3 below (with the complete detailed table in Table 3).

TABLE 3.

Summary of included studies by category.

Category	Number of studies (%)	Key findings
Study design
Empirical studies	58 (67%)	Mixed evidence; strongest for correlational relationships
Systematic reviews	18 (21%)	Consistent associations identified
Theoretical/conceptual	10 (12%)	Framework development
Mental health outcomes
Anxiety/depression	34 (40%)	Positive association with time spent; moderated by usage patterns
Social comparison/body image	18 (21%)	Strongest for image‐based platforms (Instagram, TikTok)
Sleep disruption	12 (14%)	Blue light exposure and FOMO effects
Cyberbullying victimization	11 (13%)	Association with depression, anxiety, suicidal ideation
Well‐being/positive outcomes	11 (13%)	Community support, identity affirmation
Misinformation topic
Health/COVID‐19	22 (26%)	Most extensively studied misinformation topic
Political	18 (21%)	Polarization effects well documented
General	15 (17%)	Spread patterns and correction strategies
Mental health/psychiatric	12 (14%)	Emerging literature; TikTok particularly studied; patterns across diagnostic categories
Environmental	5 (6%)	Limited evidence relative to health/politics
Mixed/other	18 (21%)	Multiple topics or unclear classification
Social media platform
Multiple platforms	36 (42%)	General patterns across platforms
Facebook	15 (17%)	Most studied for misinformation; algorithm research
Twitter/X	12 (14%)	Rapid spread; political misinformation
Instagram	10 (12%)	Mental health and body image focus
TikTok	12 (14%)	Rapidly growing focus; mental health misinformation
YouTube	5 (6%)	Algorithmic recommendation effects

4. Results

4.1. Study Characteristics

The 86 included studies comprised 58 empirical studies (67%), 18 systematic reviews (21%), and 10 theoretical/conceptual works (12%). Publication years ranged from 2018 to 2026, with concentration in 2020–2023 reflecting heightened research interest during the COVID‐19 pandemic, and additional studies from 2024 to 2026 identified through the updated search, particularly on mental health misinformation and TikTok. Geographically, studies originated primarily from North America (42%), Europe (31%), and Australia/New Zealand (15%), with limited representation from Global South contexts (12%). A summary of all 86 included studies, including design, sample, key findings, and quality ratings, is provided in Table 3. A brief summary table categorizing included studies by study design, mental health outcomes, misinformation topic, and social media platform is provided in Table 3 above.

4.1.1. Quality Summary

Quality assessment using the Joanna Briggs Institute Checklist, AXIS, and CASP tools indicated that 42 studies (49%) were rated as high quality, 31 studies (36%) as moderate quality, and 13 studies (15%) as low quality. Higher‐quality studies (systematic reviews and large‐scale observational studies) were weighted more heavily in drawing conclusions; where findings from high‐ and low‐quality studies diverged, this is explicitly noted. Sensitivity analyses excluding low‐quality studies were conducted to assess the robustness of findings. Common limitations included small sample sizes (n < 100), cross‐sectional designs (precluding causal inference), and reliance on self‐report measures.

4.2. The Dual‐Edged Sword of Connectivity

4.2.1. Evidence From Included Studies (Level 1–2)

Research consistently documents social media's capacity to build community and amplify marginalized voices. Networked counterpublics have enabled activist movements to document injustice and coordinate action with unprecedented speed and scale [1, 26]. Online communities provide vital support for individuals with rare diseases or those facing social exclusion, offering spaces for validation and knowledge sharing often unavailable offline [14, 27]. Studies have found that participation in such communities can significantly reduce feelings of isolation while increasing perceptions of social connectedness [12].

However, meta‐analytic evidence (Level 1) has also found the relationship between social media use and mental health to be small to moderate and highly context‐dependent [13, 15]. For many users, social media serves as a valuable source of social support, particularly for those with limited offline networks [12].

4.2.2. Evidence From Included Studies (Level 1–2)

At the same time, this connectivity is mediated by architectures designed for extraction. Zuboff's [3] concept of surveillance capitalism is paramount: the “attention economy” dictates that platforms maximize user engagement to harvest data and sell targeted advertising. This economic imperative explains why algorithms are designed to promote content triggering high‐arousal emotions like outrage or fear, precisely the content that is often misinformation [4]. Thus, the same infrastructure connecting a patient support group also amplifies health misinformation during a pandemic.

4.3. The Misinformation‐Mental Health Nexus

A key finding is that misinformation and mental health challenges are not separate issues but are intrinsically linked through platform design.

4.3.1. Algorithmic Amplification of Harm (Level 1–2 Evidence)

Algorithms do not merely surface falsehoods; they actively recommend them. A user expressing vaccine hesitancy may be funnelled into increasingly anti‐vaccine communities, reinforcing beliefs and isolating them from corrective information [28]. This constant exposure to threatening or polarizing content is associated with anxiety, helplessness, and “doomscrolling” ([5, 6, 7]). While these negative effects are well‐documented, meta‐analyses (Level 1) indicate that the relationship between misinformation exposure and mental health outcomes is moderated by individual factors such as media literacy, prior beliefs, and social support [13, 15].

4.3.2. Erosion of Trust and Cognitive Load (Level 2–3 Evidence)

The pervasive presence of misinformation erodes trust in institutions, experts, and interpersonal relationships. This creates epistemic uncertainty where individuals no longer know what or whom to believe. The cognitive effort required to constantly vet information is immense, leading to mental fatigue and disengagement [29, 30].

4.3.3. Comparative Suffering and Vicarious Trauma (Level 2 Evidence)

During crises, social media becomes a flood of both authentic and manipulated information. Users are exposed to curated suffering from around the world, leading to what scholars term “comparative suffering”, a phenomenon where constant exposure to others' distress diminishes one's own well‐being and vicarious trauma, both of which are significant factors in declining mental health even for those not directly affected [8].

4.4. Psychiatric Misinformation

A growing body of literature (Level 1–2 evidence) specifically examines misinformation about psychiatric conditions. Unlike general health misinformation, psychiatric misinformation involves unique characteristics: it directly affects help‐seeking behaviour, treatment adherence, and stigmatization of individuals with mental illness [10].

4.4.1. Depression and Anxiety (Level 2 Evidence)

Starvaggi et al. [9] documented that misinformation about depression frequently involves misleading claims about antidepressant side effects, misattribution of normal sadness to clinical depression, and promotion of unproven “natural” treatments. Hudon et al. [11] found that 58% of popular TikTok videos about depression contained clinically significant inaccuracies, including claims that antidepressants are “addictive” and that depression is “simply a choice”. This misinformation is associated with reduced treatment‐seeking and increased self‐stigma among adolescents [11].

4.4.2. ADHD (Level 2 Evidence)

Research by Starvaggi et al. [9] found that ADHD misinformation on social media frequently involves denial of the condition as a legitimate disorder, claims that stimulant medications “change personality”, and promotion of untested “alternative” treatments. These messages are particularly prevalent on TikTok, where #ADHD videos have accumulated over 10 billion views.

4.4.3. Personality Disorders (Level 2–3 Evidence)

Hudon et al. [11] documented extensive misinformation about personality disorders, particularly borderline personality disorder, with content frequently pathologizing normal emotional responses and promoting stigmatizing narratives.

4.4.4. Autism (Level 2–3 Evidence)

Misinformation about autism on social media includes claims about causes (vaccines, diet), misleading descriptions of autistic experience, and promotion of ineffective or harmful interventions [10].

4.4.5. Emerging Literature (Level 1 Evidence)

Recent systematic reviews confirm that psychiatric misinformation is characterized by high emotional content, exploitation of confirmation bias, and the “illusion of explanatory depth”, where simplistic explanations are perceived as deeper understanding than they provide [9]. Lorenzo‐Luaces and Starvaggi [10] argue that mental health professionals need specific competencies to identify and address this misinformation during clinical consultations.

4.5. Connectivity and Information Accessibility

4.5.1. Evidence From Included Studies (Level 2–3)

Social media has fundamentally restructured human communication, dismantling traditional barriers of geography, institution, and culture to create a hyper‐connected digital landscape [31]. These platforms have evolved beyond simple networking into dynamic ecosystems where historically marginalized voices can reach global audiences without passing through traditional gatekeepers [22, 32].

This transformation carries profound democratic potential. Movements such as #MeToo demonstrate how personal testimonies, aggregated and amplified online, can challenge entrenched power structures and achieve tangible social change [1, 2]. Indigenous communities leverage these same tools to preserve endangered languages and share cultural knowledge on their own terms [33]. Meanwhile, the unprecedented velocity of information dissemination enables life‐saving communication during crises while empowering new forms of transnational collaboration and entrepreneurship [34, 35].

As Couldry and Hepp [36] argue, digital participation is becoming constitutive of contemporary citizenship itself. Yet this connectivity is profoundly double‐edged: the same architectures that democratize access also accelerate misinformation and enable surveillance capitalism [3, 28]. The central challenge, therefore, is not simply celebrating connectivity but learning to manage it responsibly, harnessing its emancipatory potential while mitigating its risks.

4.6. Mental Health Impacts

4.6.1. Evidence From Included Studies (Level 1–2)

The impact of social media on mental health has become a growing concern, especially among younger users who are particularly susceptible to its adverse effects. A substantial body of research underscores the correlation between intensive social media use and a range of psychological issues, including anxiety, depression, low self‐esteem, and body dissatisfaction [8]. These challenges are often intensified by the unique social pressures of online environments, such as the constant need for validation and the pervasive comparison to others' curated, idealized portrayals of life.

Image‐centric platforms like Instagram, Snapchat, and TikTok are particularly problematic in this regard. The RSPH [8] found that these platforms significantly contribute to body image concerns among adolescents, especially teenage girls, by promoting unrealistic beauty standards and perpetuating feelings of inadequacy. This exposure is not incidental; it is algorithmically reinforced. Social media algorithms are designed to maximize engagement, often favoring visually appealing, emotionally charged, or idealized content. As a result, users are frequently exposed to filtered, polished representations of life and appearance, distorting perceptions of normality and self‐worth. Over time, this environment fosters an internalization of perfectionism and the belief that personal value is contingent on external validation ([5, 6, 7]).

In addition to body image concerns, the physiological effects of prolonged social media use cannot be overlooked. One major area of concern is the disruption of sleep patterns. Excessive screen time, particularly before bed, interferes with melatonin production due to blue light exposure, impairing the body's natural circadian rhythms. This sleep disruption is associated with fatigue, irritability, and cognitive impairment, which can exacerbate existing mental health problems [37, 38]. Compounding this is the phenomenon of the “fear of missing out” (FOMO), a pervasive anxiety that compels users to remain constantly connected. FOMO can contribute to chronic stress, restlessness, and diminished well‐being, as users feel excluded from experiences depicted online and pressured to maintain an unrealistic standard of social presence [8].

Cyberbullying is another critical concern linked to mental health decline among social media users. Unlike traditional bullying, cyberbullying occurs in digital spaces and transcends temporal and geographical boundaries. The anonymity afforded by social platforms often emboldens perpetrators, resulting in more severe and sustained harassment. Victims of cyberbullying frequently report heightened anxiety, depressive symptoms, and suicidal ideation. According to the Cyberbullying Research Center [39, 40], the emotional trauma caused by such harassment can persist long after the incidents themselves, affecting victims' self‐concept, interpersonal relationships, and long‐term mental health outcomes.

Adults are not immune to the psychological toll of social media. The phenomenon of “doomscrolling”, the compulsive consumption of negative news content, has emerged as a prevalent behaviour among adult users, particularly in times of crisis or societal uncertainty. This constant exposure to distressing information can create a sustained sense of fear, helplessness, and emotional fatigue, contributing to burnout and a diminished sense of agency ([5, 6, 7]).

4.6.2. Positive Aspects (Level 1–2 Evidence)

Nevertheless, it is important to acknowledge that the relationship between social media and mental health is not inherently negative. Social media platforms also offer substantial mental health benefits when used constructively. They provide spaces for connection, self‐expression, and community building, especially for individuals coping with mental illness. Online support networks can reduce stigma, offer peer support, and connect users with valuable mental health resources [12]. While these benefits are well documented, meta‐analyses (Level 1) indicate that the relationship between social media use and mental health is small to moderate and highly dependent on individual factors such as usage patterns, prior mental health, and social support [13, 15].

4.7. Democratizing Information and Encouraging Civic Engagement

4.7.1. Evidence From Included Studies (Level 1–2)

The transformation of political engagement through social media reflects what media scholars term “participatory politics”, a shift away from hierarchical, institution‐centred modes of civic action toward more horizontal, peer‐driven forms of engagement [22]. Unlike traditional models of political participation, participatory politics is defined by its interactive, expressive, and often personalized nature.

Empirical evidence supports the political mobilization effects of these platforms. Boulianne [41], through meta‐analytic studies (Level 1), found that social media users are 2.3 times more likely to attend protests and 1.8 times more likely to contact political representatives than non‐users. This shift reflects the communication theory of “differential gains”, which posits that the impact of media consumption varies based on the user's social capital, media literacy, and platform engagement styles [42].

A crucial dimension of this democratizing effect lies in epistemic access, the ability of individuals to both consume and contribute to public knowledge. Research by Starbird [43] on crisis informatics demonstrates that crowdsourced verification can sometimes outperform traditional media outlets in both speed and accuracy. During Hurricane Harvey and other natural disasters, users created and updated interactive maps of affected areas, flagged misinformation, and coordinated relief efforts [23].

However, scholars warn that this epistemic democratization is fraught with contradictions. The same affordances that enable grassroots verification can also amplify misinformation and disinformation, especially when content is emotionally charged or sensational [24]. This results in what Tufekci [44] calls “context collapse”, where the speed and volume of digital discourse can overwhelm users' ability to distinguish credible information from falsehoods. Furthermore, algorithmic curation means that political content is often filtered through echo chambers and filter bubbles [45], complicating the ideal of democratized discourse and risking increased polarization.

4.8. The Importance of Media Literacy

4.8.1. Evidence From Included Studies (Level 1–2)

Research on media literacy has evolved beyond traditional fact‐checking to encompass a broader set of competencies. Scholars argue that media literacy now includes the capacity to produce and share information responsibly, not merely to consume it [17, 46]. This “presumption” dimension requires understanding how one's own digital footprint contributes to broader narratives, including ethical considerations such as whether content may stigmatize vulnerable groups or respect privacy [47].

A second frontier in media literacy research is the ability to decode visual and multimedia content. Deepfakes, manipulated photographs, and AI‐generated videos pose new challenges to truth‐seeking [48]. Studies have shown that media‐literate individuals learn to inspect metadata, compare multiple video sources, and deploy reverse‐image search tools to detect tampering [17]. Workshops teaching these image‐forensic techniques are increasingly appearing in educational settings.

Collaborative learning models have further strengthened media literacy's impact. “Train‐the‐trainer” programs equip librarians, youth mentors, and healthcare workers with both knowledge and pedagogical tools to deliver media‐literacy sessions in their communities [46]. Research indicates that decentralized instruction, where digital‐first seniors learn from tech‐savvy grandchildren or rural educators adapt lessons to local concerns, enables media literacy to spread organically [47].

4.8.2. Emerging Evidence on Mental Health Literacy (Level 2 Evidence)

Recent studies specifically address mental health literacy as a component of media literacy. Starvaggi et al. [9] found that users with higher mental health literacy were better able to identify psychiatric misinformation and less likely to share it. Hudon et al. [11] documented that mental health literacy programs that incorporate critical digital skills, including evaluating sources of mental health information and recognizing emotional manipulation, show promise in reducing belief in psychiatric misinformation; though rigorous evaluation studies are needed.

5. Analysis and Discussion

5.1. Integrating the Evidence: The Dual‐Edged Sword of Connectivity

Research confirms social media's capacity to build community and amplify voice. Networked counterpublics have enabled activist movements like #BlackLivesMatter to document injustice and coordinate action with unprecedented speed and scale [26]. Similarly, online communities provide vital support for individuals with rare diseases or marginalized identities, offering spaces for validation and knowledge sharing that are often unavailable offline [14, 27].

However, this connectivity is mediated by architectures designed for extraction. Zuboff's [3] concept of surveillance capitalism is paramount here. The “attention economy” dictates that platforms must maximize user engagement to harvest more data and sell targeted advertising. This economic imperative explains why algorithms are designed to promote content that triggers high‐arousal emotions like outrage or fear, which is often misinformation [4]. Thus, the same infrastructure that connects a patient support group also amplifies health misinformation during a pandemic. As noted in the results, meta‐analytic evidence (Level 1) confirms these associations are small‐to‐moderate and moderated by individual vulnerability and usage patterns [15, 16], reinforcing that platform architecture interacts with user characteristics rather than determining outcomes deterministically.

5.2. The Misinformation‐Mental Health Nexus

Misinformation and mental health challenges are not separate issues but are intrinsically linked through platform design.

5.2.1. Algorithmic Amplification of Harm (Supported by Level 1–2 Evidence)

Algorithms do not merely spread falsehoods; they actively recommend them. A user expressing vaccine hesitancy may be funnelled into increasingly anti‐vaccine communities, a process that reinforces beliefs and isolates them from corrective information [28]. This state of constant exposure to threatening or polarizing content is associated with anxiety, a sense of helplessness, and “doomscrolling” ([5, 6, 7]). The strength of this evidence varies: algorithmic recommendation effects are well documented in large‐scale observational studies, while the direct pathway to mental health outcomes relies more heavily on cross‐sectional associations that require longitudinal confirmation.

5.2.2. Erosion of Trust and Cognitive Load (Supported by Level 2–3 Evidence)

The pervasive presence of misinformation erodes trust in institutions, experts, and even interpersonal relationships. This creates a state of epistemic uncertainty where individuals no longer know what or whom to believe. The cognitive effort required to constantly vet information is immense, leading to mental fatigue and disengagement [29, 30].

5.2.3. Comparative Suffering and Emotional Distress (Supported by Level 2 Evidence)

During crises, social media becomes a flood of both authentic and manipulated information. Users are exposed to curated suffering from around the world, leading to what scholars term “comparative suffering”, a phenomenon where constant exposure to others' distress diminishes one's own well‐being and vicarious trauma, both of which are significant factors in declining mental health even for those not directly affected [8].

5.3. Mental Health Misinformation in Clinical Context

The prevalence of mental health misinformation on social media has direct implications for clinical practice [10, 11]. As documented in the results (Section 4.4), psychiatric misinformation spans diagnostic categories including depression, anxiety, ADHD, personality disorders, and autism. This misinformation is characterized by high emotional content, exploitation of confirmation bias, and the “illusion of explanatory depth”, where simplistic explanations are perceived as deeper understanding than they provide [9].

Common patterns identified in the literature include:

• Diagnostic Misattribution: Users misinterpreting normal experiences (e.g., sadness, anxiety, distractibility) as indicators of clinical disorder, leading to self‐diagnosis [9].

• Treatment Misinformation: False claims about medication side effects, “natural” cures, and withdrawal from evidence‐based treatments [11].

• Stigmatizing Content: Misinformation that reinforces negative stereotypes about psychiatric conditions, contributing to self‐stigma and reduced help‐seeking [10].

5.3.1. Emerging Digital Mental Health Content Creators

A significant recent development is the emergence of “mental health influencers”, often non‐professionals who create content about psychiatric conditions. Starvaggi et al. [9] found that content from these influencers receives substantial engagement but frequently contains inaccuracies. The “illusion of explanatory depth” is particularly relevant; influencers who present confident, simplified explanations may be trusted over professionals who communicate nuance and uncertainty [9].

5.3.2. Clinical Competencies Required

Lorenzo‐Luaces and Starvaggi [10] argue that mental health professionals require specific competencies to address this challenge, including: (1) knowledge of common mental health misinformation topics, (2) skills in assessing patients' online information sources, (3) ability to discuss online health information during consultations without appearing dismissive, and (4) capacity to recommend reliable digital resources. These competencies are not currently standard in most training programs, representing a significant gap in professional preparation.

5.4. The Critical Role of Media Literacy: Beyond Fact‐Checking

The standard prescription of “media literacy” is often reduced to checklist fact‐checking. However, our analysis shows this is inadequate against algorithmic manipulation. Pangrazio [17] argues that traditional media literacy is insufficient because it treats literacy as a static skill for content evaluation while neglecting the dynamic algorithmic forces that shape user experience and emotional response.

Drawing on Pangrazio's [17] framework and supported by the literature reviewed here, we propose that Critical Digital Literacy (CDL) offers a more robust approach. The conceptual novelty of this framework, as distinguished from existing health literacy and digital health literacy models, lies in its integration of three interconnected competencies that address gaps in existing frameworks:

• Algorithmic Awareness: Understanding that what users see is not a neutral reflection of reality but a curated feed designed to keep them engaged. This involves questioning why a certain post is appearing now and what the platform's incentive might be.

• Data Literacy: Understanding the business model of data extraction and how personal information is used to manipulate feed content and target ads.

• Emotional Literacy: Recognizing the emotional triggers (outrage, fear, envy) that make content go viral and that are exploited by both malicious actors and the platform's own algorithms. This self‐awareness is a key defense against manipulation and a direct protector of mental well‐being.

These competencies extend beyond existing digital health literacy frameworks [18, 19], which focus primarily on information‐seeking and content evaluation, by explicitly addressing the algorithmic and economic forces that shape content exposure and emotional responses.

5.5. Implications for Mental Health Professionals

The findings have significant implications for clinical practice. Mental health professionals increasingly encounter patients who have been exposed to psychiatric misinformation online [11]. Based on the synthesis, we propose the following practice recommendations:

1. Assessment of Digital Information Sources: Clinicians should routinely ask about patients' online information‐seeking, including platforms used, topics searched, and specific claims encountered. This information can inform clinical formulation and treatment planning [10].

2. Discussing Online Misinformation in Consultations: Hudon et al. [11] recommend approaching discussions of misinformation with curiosity rather than confrontation. Strategies include: (a) validating patients' concerns about their health, (b) exploring the source and basis of online claims, (c) providing accessible evidence‐based alternatives, and (d) avoiding dismissive responses that can alienate patients.

3. Recommending Reliable Digital Resources: Clinicians should maintain a curated list of reliable online resources for mental health information [10]. These may include peer‐reviewed patient information sites, official guidelines, and vetted patient communities.

4. Professional Competencies: Training programs should incorporate competencies for addressing digital health information, including: knowledge of common mental health misinformation topics, skills in empathic communication about patients' online sources, and familiarity with evaluating digital health content [11].

5. Supporting Digital Resilience in Patients: Clinicians can help patients develop critical evaluation skills, including questioning the motivation behind content (e.g., commercial interests), seeking multiple sources, and identifying signs of misinformation (e.g., unsupported claims, anecdotal evidence presented as fact) [9].

5.6. Implications for Educators, Parents, Policymakers, and Platform Designers

For Educators and Parents: Move beyond fact‐checking to integrate activities building algorithmic and emotional awareness; adopt pedagogies of play using simulations and gamified learning. Evidence from systematic reviews supports literacy education, though specific “pedagogies of play” approaches require further evaluation.

For Policymakers and Platform Designers: Advocate for algorithmic transparency, well‐being‐by‐design principles, and regulatory frameworks that reduce harmful content amplification. Support research into platform‐level interventions (e.g., friction tools, content warning prompts) and monitor emerging evidence on alternative platform models. Evidence for specific regulatory approaches is emerging but requires further evaluation.

5.7. Future Research Priorities

The synthesis reveals several critical gaps requiring research attention:

1. Intervention Evaluation

   • Rigorous randomized controlled trials evaluating interventions to improve mental health misinformation literacy across different age groups and settings [11].
   • Testing the effectiveness of specific educational strategies (e.g., pedagogies of play, gamified learning) compared to traditional approaches.
   • Evaluating whether improved literacy translates to reduced psychological distress and better health outcomes.
2. Healthcare Professional Education

   • Developing and validating curricula for mental health professionals training in identifying and addressing psychiatric misinformation [10].
   • Testing the effectiveness of continued professional development programs in this area.
   • Evaluating whether clinician competency is associated with patient outcomes.
3. Platform‐Level Interventions

   • Assessing the effectiveness of “friction” tools (e.g., content warning prompts, delay mechanisms) in reducing engagement with harmful content [9].
   • Evaluating the impact of algorithmic transparency features on user understanding and behaviour.
   • Monitoring platform cooperativism models as structural interventions [49], recognizing these remain conceptual proposals requiring empirical evaluation.
4. Longitudinal Studies

   • Prospective cohort studies examining the cumulative effects of misinformation exposure on mental health over time.
   • Studies examining developmental trajectories, particularly the critical adolescent period of vulnerability.
   • Research on the persistence of misinformation effects after correction.
5. Cross‐Cultural Research

   • Studies examining how cultural contexts moderate the relationship between social media use, misinformation, and mental health.
   • Research on mental health misinformation patterns in non‐Western contexts.
   • Comparative studies of different regulatory approaches to misinformation.
6. Specific Psychiatric Conditions

   • Detailed examination of misinformation patterns for specific disorders (depression, anxiety, ADHD, psychosis, and eating disorders).
   • Studies of misinformation about different treatment modalities (pharmacological, psychological, alternative).
   • Research on the relationship between misinformation and treatment adherence.
7. Novel Methodologies

   • Implementation of digital trace data to examine real‐time misinformation exposure.
   • Use of natural experiments to assess the effects of platform policy changes.
   • Integration of qualitative and quantitative methods to capture both patterns and experiences.

5.8. Limitations

Several limitations of this review must be acknowledged. First, the restriction to English‐language publications may have excluded relevant research from non‐English speaking contexts, potentially limiting the generalizability of findings. Second, the reliance on observational studies means that causal claims cannot be definitively established. Third, while quality assessment and sensitivity analyses provide some control for confounding, the possibility of unmeasured confounders remains. Fourth, the heterogeneity in study designs, outcome measures, and definitions limited the extent to which meta‐analytic synthesis was possible. Fifth, the predominance of studies from Western, educated, industrialized, rich, and democratic (WEIRD) societies limits the generalizability of findings to other cultural contexts. Sixth, while publication bias analyses were conducted, the “file drawer” problem cannot be fully eliminated; it remains possible that unpublished null findings exist that would attenuate the observed effects. Seventh, the review covers studies published between 2018 and 2026; changes in social norms, platform policies, and content moderation practices over this period may affect the comparability of findings across time. Eighth, as noted in the results, the evidence base for psychiatric misinformation is relatively recent and emerging, with fewer high‐quality longitudinal studies available compared to general misinformation research. Despite these limitations, the review's methodological rigour, including comprehensive searching, independent double screening, validated quality assessment, and sensitivity analyses, enhances confidence in the findings.

5.9. Summary

This systematic review demonstrates that the challenges of misinformation and mental well‐being on social media are deeply intertwined, linked to the same underlying economic and architectural models. The synthesis reveals that while social media offers significant benefits for connectivity, community building, and civic engagement, these benefits are shadowed by platform architectures designed for engagement extraction that simultaneously amplify misinformation and contribute to psychological distress.

A key contribution of this review is the synthesis of emerging literature on psychiatric misinformation specifically, which has unique characteristics, including direct effects on help‐seeking behaviour, treatment adherence, and stigmatization, that distinguish it from general misinformation. The findings indicate that conventional media literacy approaches focused solely on fact‐checking are insufficient; instead, a Critical Digital Literacy framework encompassing algorithmic awareness, data literacy, and emotional literacy is essential for building user resilience.

Based on this synthesis, we propose three integrated strategies:

1. Implement Pedagogies of Play: Move beyond theoretical literacy lessons. Educational programs should use hands‐on, experiential learning where users interact with and even “break” simulated algorithms to understand their logic. This proposal, supported by conceptual literature, requires empirical evaluation to establish effectiveness.

2. Advocate for Algorithmic Transparency and Well‐Being by Design: Policy and advocacy must push for regulations that force platforms to offer users meaningful choices and transparency. This includes options for chronological feeds, clear indicators of why content is recommended, and the integration of “friction” tools (e.g., prompts to reconsider sharing unvetted content, time management tools). Evidence for specific regulatory approaches is emerging but requires further evaluation.

3. Support Research into Alternative Platform Models: Monitor and evaluate alternative social media models that are user‐owned and operated, or that prioritize public value over shareholder profit. These models represent a theoretical approach requiring empirical evaluation of their effectiveness in reducing misinformation and supporting well‐being.

Through framing media literacy not as a mere skill but as a critical understanding of digital ecosystems, we can better equip individuals to navigate social media, transforming them from passive consumers into empowered, resilient, and critical digital citizens. The implications for educators, mental health professionals, policymakers, and platform designers are substantial, requiring coordinated action across individual, professional, and structural levels.

6. Conclusion

Social media has significantly reshaped global communication, offering new opportunities for connection, expression, and civic participation. It has served as a vital platform for marginalized groups to challenge dominant narratives, build communities, and mobilize for social change. Movements such as #BlackLivesMatter and #MeToo exemplify how digital spaces can support collective action and amplify underrepresented voices [1, 2].

Nevertheless, the widespread use of social media has also introduced substantial challenges. The rapid spread of misinformation [4, 28], including psychiatric misinformation specifically, which affects help‐seeking behaviour, treatment adherence, and stigmatization [9, 11], and the decline in critical engagement have undermined public discourse. Additionally, the psychological effects of constant comparison, curated realities, and addictive engagement mechanisms have been associated with rising levels of anxiety and depression, particularly among young people ([5, 6, 7, 8]). Meta‐analytic evidence confirms these associations are small to moderate and moderated by individual vulnerability and usage patterns [15, 16], reinforcing that platform architecture interacts with user characteristics rather than determining outcomes deterministically.

In response to these issues, media literacy emerges as a necessary strategy. Through fostering critical thinking, evaluative judgement, and ethical digital participation, media literacy equips individuals to engage more responsibly online. It empowers users to question sources, resist manipulation, and contribute to constructive discourse [17, 46]. However, as this synthesis has demonstrated, conventional media literacy approaches focused solely on fact‐checking are insufficient against algorithmic manipulation. A Critical Digital Literacy framework encompassing algorithmic awareness, data literacy, and emotional literacy is essential for building user resilience in the contemporary digital environment. Promoting media literacy requires collaboration across education, government, and the tech industry. Educational systems should integrate it into curricula, policymakers should support digital literacy initiatives, and platforms must design tools that prioritize transparency and user empowerment [49].

This synthesis demonstrates that the challenges of misinformation and mental well‐being on social media are deeply intertwined, linked to the same underlying economic and architectural models. The findings further reveal that psychiatric misinformation represents a distinct and growing concern, with unique characteristics, including direct effects on clinical outcomes that require targeted attention from mental health professionals. Therefore, solutions must be equally integrated, targeting both individual agency and structural reform.

6.1. Based on This Synthesis, We Propose Three Integrated Strategies

1. Implement Pedagogies of Play: Move beyond theoretical literacy lessons. Educational programs should use hands‐on, experiential learning where users interact with and even “break” simulated algorithms to understand their logic. This could involve games that show how engagement metrics drive virality or exercises in creating counter‐narratives to misinformation [17]. This proposal is supported by conceptual literature and initial pilot studies but requires rigorous empirical evaluation to establish effectiveness.

2. Advocate for Algorithmic Transparency and Well‐Being by Design: Policy and advocacy must push for regulations that force platforms to offer users meaningful choices and transparency. This includes options for chronological feeds, clear indicators of why content is recommended, and the integration of “friction” tools (e.g., prompts to reconsider sharing unvetted content, time management tools). Evidence for specific regulatory approaches is emerging but requires further evaluation through implementation research.

3. Support Research into Alternative Platform Models: Monitor and evaluate alternative social media models that are user‐owned and operated, or that prioritize public value over shareholder profit. These models represent a theoretical approach requiring empirical evaluation of their effectiveness in reducing misinformation and supporting well‐being [49]. This is a promising future direction rather than an established intervention.

6.2. Implications for Mental Health Professionals

The synthesis has particular relevance for clinical practice. Mental health professionals increasingly encounter patients exposed to psychiatric misinformation online [11]. Clinicians require specific competencies to: (a) assess patients' online information sources, (b) discuss inaccurate psychiatric information encountered online without dismissing patient concerns, (c) recommend reliable digital mental health resources, and (d) support patients in developing critical evaluation skills for online health content [10]. Training programs should incorporate these competencies to prepare professionals for the realities of contemporary practice.

6.3. Implications for Educators, Parents, Policymakers, and Platform Designers

The findings also have substantial implications across multiple stakeholder groups. Educators and parents should move beyond fact‐checking to build algorithmic and emotional awareness through experiential learning. Policymakers should advocate for algorithmic transparency and well‐being‐by‐design principles while supporting research into platform‐level interventions. Platform designers should integrate “friction” tools and transparency features that empower users to make informed choices about their engagement.

This paper provides a scaffold for future primary research to test the efficacy of these proposed interventions. Through framing media literacy not as a mere skill but as a critical understanding of digital ecosystems, we can better equip individuals to navigate social media, transforming them from passive consumers into empowered, resilient, and critical digital citizens. The integration of individual‐level competencies with structural advocacy offers the most promising pathway for addressing the interconnected challenges of misinformation and mental well‐being in the digital age.

Author Contributions

The author was responsible for the conceptualization, literature review, analysis, and writing of the manuscript.

Funding

The author has nothing to report.

Ethics Statement

This article does not contain any studies with human or animal participants.

Consent

The author has nothing to report.

Conflicts of Interest

The author declares no conflicts of interest.

Al Musawi H. K., “Navigating Social Media: Balancing Connectivity With Media Literacy to Combat Misinformation and Protect Mental Well‐Being,” Health Promotion Journal of Australia 37, no. 4 (2026): e70243, 10.1002/hpja.70243.

Data Availability Statement

The data supporting this study are available upon reasonable request from the corresponding author.