A
Problematic Social Media Use and Anxiety: A Literature Review and Conceptual ModelA
ASahu1
VShukla2
KGillespie3
TobinS.J4
BartlettS.E1
Professor
SelenaBartlett1✉Emailselena.bartlett@qut.edu.au1School of Clinical Sciences, Faculty of HealthQueensland University of TechnologyBrisbaneAustralia
2Department of PsychiatryAll India Institute of Medical SciencesNew DelhiIndia
3School of Clinical Sciences, Faculty of HealthQueensland University of Technology (QUT)BrisbaneAustralia
4School of Psychology and Counselling, Faculty of HealthQueensland University of Technology (QUT)BrisbaneAustralia
5School of Clinical Sciences, Faculty of Health, Translational Research InstituteQueensland University of Technology (QUT)BrisbaneAustralia
6The Mater Medical ResearchThe University of QueenslandBrisbane
Sahu A1, Shukla V,2 Gillespie K,3 Tobin S.J.4, Bartlett S.E5
1. School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Australia
2. Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India,
3. School of Clinical Sciences, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Australia
4. School of Psychology and Counselling, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Australia
5. School of Clinical Sciences, Faculty of Health, Translational Research Institute, Queensland University of Technology (QUT), Brisbane, Australia; The Mater Medical Research, The University of Queensland, Brisbane,
Correspondence:
Professor Selena Bartlett
selena.bartlett@qut.edu.au
Abstract
With over 5.24 billion active accounts globally, social media platforms significantly shape emotional experiences. Problematic social media use (PSMU), which has been defined as a maladaptive pattern of compulsive checking and preoccupation, is consistently linked with increased anxiety. However, this association varies depending on the user, their usage patterns, and the online environment. The literature review aimed to identify the behavioural and subjective markers linking PSMU to anxiety-like symptoms and synthesise these findings into a unified, mechanistic model. Following PRISMA principles, an intensive literature mapping process was conducted, resulting in the retention and synthesis of 80 empirical studies.
A
The synthesis generated a holistic conceptual model identifying five primary mechanistic pathways through which social media use contributes to anxiety. The most consistently supported mechanisms are Social Evaluation Threat (n = 42), Overload leading to Fatigue (n = 17), Intolerance of Uncertainty and Perceived Lack of Control (n = 11), and Mood Regulation and Absorption (n = 11). Sleep Disruption (n = 7) was identified as a critical meta-mediator, amplifying downstream anxiety. Furthermore, Life Events (n = 14) function as a meta-moderator, shaping the severity and direction of the pathways. Importantly, there are consistently bidirectional relationships, where anxiety acts as both a precursor and a consequence of problematic engagement, creating self-reinforcing cycles. This review advances a novel relational, mechanistic model that moves beyond simple exposure models of social media use. This model offers a guide for future longitudinal research and provides direct implications for targeted interventions and safer platform design policiesA
1. Introduction
Globally, around 4% of people experience anxiety, a condition that increases vulnerability to depression, substance use, suicidal ideation, and long-term health problems (WHO, 2025). With over 5.24 billion active social media accounts and average daily use approaching three hours (Digital 2025 Global Overview Report), these platforms now play a significant role in shaping emotional experiences. As social media becomes increasingly embedded in daily behaviour, there is a growing scientific focus on how its use relates to anxiety and associated psychological outcomes (Corke et al., 2025; Ventriglio et al., 2024; Yue & Rich, 2023). Although digital platforms can provide connection and information, frequent or problematic use is consistently linked with increased anxiety (Lai et al., 2023; Shanon et al., 2022; Zhou et al., 2023). These associations vary depending on who uses social media, how they use it, and the characteristics of the online environments they engage with (Yue & Rich, 2023; Sala et al., 2024; Perlmutter et al., 2024).
Problematic social media use (PSMU) refers to a maladaptive pattern of social media engagement characterised by compulsive checking, preoccupation, and difficulty controlling use despite negative consequences. Anxiety itself is a broad construct that includes social anxiety, general anxiety, worry, and intolerance of uncertainty. Evidence across diverse populations links heightened social media engagement with increased internalising symptoms, particularly during adolescence and early adulthood (Hylkilä et al., 2024; WHO, 2024). Facebook addiction, for example, predicts lower life satisfaction through increased social anxiety and depressive symptoms (Foroughi et al., 2019), while reduced offline support increases vulnerability to problematic involvement (Lei et al., 2018) and heavy use is associated with greater odds of depression in large samples (Lin et al., 2016). A synthesis of studies examining adolescents similarly shows that time online, passive engagement, emotional investment, and addictive patterns reliably correspond with distress (Keles et al., 2019).
Anxiety also shapes how individuals use these platforms. People with elevated social anxiety tend to browse rather than post, rely on anonymous accounts, and engage in self-presentation management to avoid judgment (Lai et al., 2023; O’Day & Heimberg, 2021). Anxiety may act as both a precursor and a consequence of problematic engagement, creating reinforcing cycles of avoidance, dependence, and distress (Wang et al., 2023). Several interlocking psychological processes help explain these bidirectional links. Social evaluation pressures driven by appearance-centric content, public metrics, and peer comparison promote heightened concern about how one is perceived (Chen et al., 2020; Li et al., 2024), particularly among younger users and individuals with low self-esteem.
The rapid stream of digital content can overwhelm cognitive systems, resulting in fatigue and poorer self-regulation (Brandtner et al., 2021; Riehm et al., 2019 ). Using social media for distraction, reassurance, or connection can help temporarily, but it often leads to more rumination, longer browsing, and increased reliance on others’ reactions online (Bányai et al., 2017; Brand et al., 2019; Casalé et al., 2024; Fioravanti et al., 2024). The unpredictability of online environments, shifting feedback, and ambiguous cues can increase vigilance and compulsive checking, particularly in individuals with lower inhibitory control or high intolerance of uncertainty. (Boers et al., 2020; Brand et al., 2019; Zsido et al., 2020).
These processes align with several theoretical models. The I-PACE model conceptualises problematic or anxiety-linked engagement as arising from interactions between individual predispositions, affective and cognitive responses, and diminished executive control (Brand et al., 2019). The Compensatory Internet Use Theory argues that users turn to social media to manage distress or unmet needs, inadvertently reinforcing anxiety through cycles of relief-seeking (Casale et al., 2024). Self-Determination Theory emphasises how unmet needs for relatedness, competence, or validation heighten sensitivity to evaluation and uncertainty (Dadiotis & Roussos, 2024), while Social Comparison Theory explains how idealised content and peer metrics intensify evaluative concerns (Irmer & Schmiedek, 2023). Evidence shows that social-media–related anxiety arises from several interacting mechanisms rather than a single source. This review outlines a unified framework demonstrating how individual vulnerabilities and context shape social media’s contribution to anxiety.
2. Methods
This review aimed to identify behavioural and subjective markers linking PMSU to anxiety-like symptoms and to synthesise these into a set of interconnected mechanistic pathways. Although not a formal systematic review, we followed PRISMA principles to ensure transparency in search, screening, and synthesis.
2.1 Search Strategy and Data Collection
An intensive literature mapping process was conducted using two major databases: EBSCOhost (30 databases searched; 62 results) and Scopus (45 shortlisted papers). Searches combined terms relating to:
Social media platforms: “social media”, Facebook, Instagram, TikTok
Anxiety constructs: “anxi*”, social anxiety, generalised anxiety, distress
Behavioural descriptors: passive/active use, checking, time online, usage patterns
Subjective constructs: fear of missing out (FoMO), social comparison, mood, self-esteem
Snowballing (citations + reference list scanning) was used to capture additional studies. COVID-19–specific studies were excluded due to strong contextual confounding from lockdown conditions. After screening and exclusions, 80 studies were retained for review.
2.2 Eligibility Criteria
Inclusion criteria
Studies examining PSMU AND psychological distress, anxiety, or anxiety-like symptoms
Studies reporting behavioural markers (e.g., passive scrolling, checking frequency) or subjective markers (e.g., worry, FoMO, rumination)
Quantitative, qualitative, or mixed methods
Peer-reviewed empirical studies
Exclusion criteria
Studies focused on COVID-19 lockdown effects
Studies unrelated to social media
Non-empirical or commentary-only papers
2.3 Screening and Selection
Following deduplication, titles and abstracts were screened for relevance. Full texts were then assessed against eligibility criteria. Decisions were refined iteratively until conceptual saturation was reached or when new searches no longer produced new markers.
2.4 Data Extraction
We extracted information on the following features to investigate the relationship between PSMU and anxiety:
Behavioural markers (passive use, active use, night-time use, immersion, checking)
Subjective markers (FoMO, worry, rumination, comparison, low self-esteem, fear of negative evaluation)
Moderators (sleep disruption, loneliness, life events)
Outcome variables (anxiety, distress, internalising symptoms)
Because definitions varied widely and constructs were often conflated in the literature, extraction remained iterative and flexible. Validated screening tools identified across studies included the Generalized Anxiety Disorder Scale-7 (GAD-7; n = 7), the Patient Health Questionnaire-9 (PHQ-9; n = 5), the Rosenberg Self-Esteem Scale (n = 5), the Depression Anxiety Stress Scales-21 (DASS-21; n = 4), and the Bergen Social Media Addiction Scale (BSMAS; n = 4).
2.5 Synthesis Approach
A conceptual mapping synthesis was used in place of a meta-analysis owing to heterogeneity in study designs, measures, and definitions. Studies were organised around recurring behavioural and subjective processes, cross-study associations with anxiety-like symptoms, and the moderators influencing these relationships.
This process generated five primary mechanistic pathways and two meta-processes, which informed the holistic conceptual model presented in Fig. 1.
2.6 Methodological Considerations
This approach emphasises relationality rather than causation, reflecting the bidirectional nature of PSMU and anxiety. Western populations dominate the evidence base, limiting generalisability. Most studies rely heavily on self-report, with limited objective behavioural or digital-trace measures.
3. Results
After exclusions, 80 studies remain. Key trends from these studies can be divided into 5 mechanisms and the meta-moderator Life Events. See Table 1 for the exact distribution of articles per mechanism.
Key Mechanism Pathways | Number of Articles* | References |
|---|---|---|
Social Evaluation Threat | 42 | Shabahang et al., 2022; Mills et al., 2018; Reich et al., 2024; Faelens et al., 2019; Mougharbel et al., 2023; Luo & Hu 2022; Agyapong-Opoku et al., 2025; Alsuni & Latif, 2020; Anto et al., 2023; Apaolaza et al., 2019; Ariefdjohan et al., 2025; Brandao & Denny 2024; Burgess 2025; Caner et al., 2022; Charmaraman et al., 2021; Chochol et al., 2023; Dodan & Negru-Subtirica 2025; Duan et al., 2020; Einstein et al., 2023; Ekinci & Akat 2023; Fabio & Tripodi 2024; Hilty et al., 2023; Honnekari et al., 2017; Huang et al., 2025; Jiménez et al., 2025; Jin et al., 2024; Juel et al., 2025; Kosola et al., 2024; Lee-Won et al., 2015; Litan 2025; Manjanatha et al., 2025; Papapanou et al., 2023; Prasad et al., 2023; Qin et al., 2024; Qiu et al., 2025; Ruan et al., 2025; Saleem et al., 2024; Seabrook et al., 2016; Świątek et al., 2021; Tingrong et al., 2025; Twomey & O’Reilly 2017; Wu et al., 2025 |
Lack of Control | 11 | Sharma et al., 2023; Weng et al., 2025; Apaolaza et al., 2019; Du et al., 2024; Erliksson et al., 2020; Herrell & Foster, 2025; He et al., 2022; Qiu et al., 2025; Reed & Haas 2025; Robertson et al., 2023; Yousef et al., 2025 |
Sleep Disruption | 7 | Luo et al., 2022; Ahmed et al., 2025; Bhat et al., 2018; Fassi et al., 2024; Herrell & Foster, 2025; Saha et al., 2025; Saleem et al., 2024 |
Overload/Fatigue | 17 | Dhir et al., 2018; Sharma et al., 2023; Weng et al., 2025; Becker et al., 2013; Brandao & Denny 2024; Ekinci & Akat, 2023; Feng et al., 2025; Herrell & Foster, 2025; He et al., 2022; Huang et al., 2025; Li et al., 2023; Li & Fan 2022; Liu et al., 2024; Mao & Liao 2025; Prasad et al., 2023; Świątek et al., 2021; Yousef et al., 2025 |
Mood Regulation & Absorption | 11 | Faelens et al., 2019; Alfredson et al., 2024; Angelini & Gini, 2024; Brailovskaia et al., 2018; Fassi et al., 2024; Juel et al., 2025; Lopes et al., 2022; Qiu et al., 2025; Roberts & David 2023; Yang et al., 2023; Yousef et al., 2025 |
Life Events | 14 | Gingras et al., 2023; Gordesli et al., 2024; Anto et al., 2023; Alfredson et al., 2024; Jahrami et al., 2022; Jin & Le 2024; Joiner et al., 2013; Lai et al., 2023; Rutter et al., 2021; Saha et al., 2025; Shaw et al., 2015; Vagka et al., 2024; Vannucci et al., 2017; Xie & Wang 2024 |
| *Some articles have multiple mechanisms | ||
Across the included studies, the most significant proportion focused on social evaluation threat (n = 42), followed by overload leading to fatigue (n = 17), lack of control (n = 11), mood regulation and absorption (n = 11), and sleep deprivation (n = 7). Additionally, 14 studies specifically explored the role of life events in relation to social media–related anxiety, highlighting the increasing interest in how external stressors interact with digital environments. Notably, only a small subset of studies examined behavioural markers of social media use, and even among these, operationalisation was often vague or inconsistently defined. Most of the field continues to rely heavily on subjective self-report indicators, with limited incorporation of digital trace data or objective behavioural measures.
Several studies also identified bidirectional relationships, indicating that social media use both influences and is influenced by existing psychological states (e.g. anxiety, PSMU). However, the strength and direction of these effects varied across contexts. In terms of sampling, the dataset is heavily skewed toward WEIRD populations, with most studies conducted in the United States (n = 26), Switzerland (n = 16), England (n = 14), Canada (n = 5), the Netherlands (n = 3) and New Zealand (n = 2), with only isolated studies from Saudi Arabia, Iran, Poland, India and China. This geographical concentration suggests limited cultural diversity and raises concerns about the generalisability of findings beyond Western, industrialised settings.
Not all studies utilised screening questionnaires to measure social media anxiety and related outcomes. Across studies examining social media use and psychological outcomes, a diverse range of validated screening tools were utilised. Anxiety and depression measures were the most employed overall. Sleep and loneliness constructs also featured prominently, with the PSQI (n = 3) and UCLA Loneliness Scale (n = 2) used across multiple studies. Measures of social appearance anxiety (e.g., SAAS), nomophobia, and social functioning each appeared twice, while many more specialised or domain-specific scales, such as emotion regulation, interpersonal trust, appearance-focused measures, and Facebook-specific metrics, were used only once. Overall, the distribution suggests that while a small cluster of core mental health screening tools dominates the field, there is substantial heterogeneity in the assessment of social media–related constructs, with many studies relying on unique or less frequently used instruments to capture specific psychosocial mechanisms.
Conceptual Model of PSMU and Social Media–Related Anxiety
The conceptual model illustrates how PSMU relates to social media–related anxiety through five interconnected pathways, each represented by a different colour in the figure. The colour-coded pathways converge on anxiety-related outcomes, illustrating that PSMU is linked to anxiety through a single route. Instead, anxiety arises from multiple, interacting, mutually reinforcing processes. This model visually and conceptually captures those interactions, providing a holistic map of how behavioural, cognitive, emotional, and contextual markers jointly shape social media–related anxiety. Although presented separately for clarity, these pathways interact continuously in real-world settings. The model is built on markers identified across the literature, distinguishing mediators, which explain how an effect occurs, from moderators, which influence when or for whom a pathway is stronger or weaker. Sleep disruption functions as a meta-mediator, arising from multiple upstream behaviours and intensifying downstream anxiety. Life events operate as a meta-moderator, shaping the severity and direction of all pathways and influencing how strongly PSMU translates into anxiety.
Fig. 1
Conceptual Model of PSMU and Social Media–Related Anxiety
This figure shows the relationship between social media use and social media-related anxiety based on markers. Moderators are markers that change the strength or direction of the effect of each pathway. Mediators are markers that explain how an effect occurs.
Social Evaluation Threat
This pathway includes fear of negative evaluation, upward social comparison, appearance concerns, and judgement sensitivity. Exposure to comparison- or evaluation-focused content consistently increases worry, self-scrutiny, and social anxiety.
Lack of Control
Mechanisms include compulsive checking, difficulty disengaging, attentional capture, and a perceived loss of agency over use. These processes heighten unpredictability and reinforce repetitive monitoring, increasing anxiety.
Overload to Fatigue
Information overload, message pressure, cognitive burden, and emotional saturation fall within this pathway. These factors contribute to attentional fatigue, reduced coping capacity, irritability, and greater susceptibility to anxiety.
Mood Regulation and Absorption
This pathway reflects using social media for escape, self-soothing, or emotional regulation, as well as deep psychological immersion in feeds. Although sometimes briefly relieving, these behaviours can entrench avoidance, disrupt sleep, and worsen anxiety over time.
Sleep Disruption
Sleep disruption sits centrally in the model. It is triggered by several pathways (including absorption, overload, and late-night use) and heightens vulnerability to anxiety. Because it amplifies the effects of other mechanisms, it functions as a higher-order mediator.
Cross-Cutting Meta-Mechanisms
Life Events (Meta-Moderator)Life events (e.g., academic stress, interpersonal conflict, major transitions) influence the strength, direction, and timing of all pathways. They affect both reliance on social media during stressful periods and sensitivity to anxiety-related outcomes, shaping how PSMU translates into psychological distress.
Discussion
The present review moves beyond simple dose–response models of social media use to propose a relational, mechanistic framework for PSMU and anxiety. Across the included studies, the association between PSMU and anxiety is neither uniform nor linear. Instead, it is shaped by a set of interacting moderators, such as platform use and contextual factors, fear of missing out (FOMO), loneliness, gratification needs, stress/distress, and broader dispositional susceptibility, which alter the strength and direction of multiple mediating pathways. Sleep disruption emerges as a meta-mediator, while social evaluation threat, overload and fatigue, intolerance of uncertainty/lack of control, and mood regulation/absorption represent core mechanisms through which PSMU contributes to anxiety. Life events and anxiety itself then function as higher-order modifiers, embedding these mechanisms in a broader developmental and situational context. Together, this conceptual map clarifies how and for whom social media becomes psychologically harmful, highlighting both points of vulnerability and potential targets for prevention and intervention.
5.1. Moderators of the PSMU–Anxiety Relationship
A consistent finding across the literature is that PSMU does not exert uniform psychological effects. Instead, a set of moderators, platform use and context, FoMO, loneliness, gratification needs, stress, and individual susceptibility, shape the conditions under which PSMU becomes anxiety-provoking. These variables rarely operate as mechanisms themselves. Rather, they magnify or buffer the mediating pathways that translate PSMU into anxiety and often participate in self-reinforcing cycles where vulnerability increases PSMU and PSMU further heightens anxiety.
FoMO emerged across multiple studies as a key correlate of social-media–related anxiety (Dhir et al., 2018; Świątek et al., 2021; Wu et al., 2025). Within the present framework, FoMO is best conceptualised as a moderator rather than a primary mediator. It reliably intensifies sensitivity to socially threatening cues, increases emotional reactivity to online interactions, and amplifies the association between PSMU and anxiety. For example, FoMO heightens vigilance toward social evaluation and intensifies distress when users perceive themselves as excluded or overlooked (Einstein et al., 2023).
Although several studies position FoMO as a mediator, for example, between PSMU and fatigue or trait anxiety, these effects are inconsistent across populations (Świątek et al., 2021; Dhir et al., 2018; Wu et al., 2025). The strength of FoMO as a predictor varies considerably: in some work, it appears weaker than compulsive use, whereas in others it predicts online social anxiety directly. Qualitative and quantitative evidence also point to both risk and buffering effects. In some contexts, FoMO is associated with vigilance and social overload, while in others, practices like authentic self-presentation mitigate FoMO-related anxiety (Anto et al., 2023; Dodan & Negru-Subtirica, 2025; Chochol et al., 2023; Duan et al., 2020). Overall, the evidence suggests that FoMO is a context-dependent amplifier whose influence depends on platform type, user characteristics, and co-occurring vulnerabilities. As such, FoMO functions more reliably as a cross-cutting moderator than as a stable mediator.
Loneliness, characterised by social relationships that are limited in number or lacking in perceived quality, are linked to FoMO and the desire for acceptance, exerts broad moderating effects across the identified mechanisms (Luo & Hu, 2022; Chochol et al., 2023; Kosola et al., 2024; Seabrook et al., 2016). Its influence is most substantial within the social evaluation threat pathway, where loneliness heightens sensitivity to comparisons, negative feedback, and perceived exclusion. Empirical evidence shows that loneliness reinforces attachment anxiety, mobile social media dependence, and sleep disturbance, creating downstream effects that compound anxiety (Luo & Hu, 2022; Papapanou et al., 2023). Evidence for benefits such as positive online interactions is mixed, and the use of cross-sectional studies limits causal conclusions. Loneliness consistently influences outcomes, though its effects differ across contexts.
Platform features and usage patterns, frequency, passive or active engagement, content type, night-time use, consistently moderate anxiety outcomes (Weng et al., 2025; Angelini & Gini, 2024; Ariefdjohan et al., 2025; Gingras et al., 2023; Hilty et al., 2023; Shaw et al., 2015). Highly visual platforms intensify social evaluation pressure; passive use increases comparison and rumination; and nighttime engagement predicts sleep disruption, a meta-mediator. However, positive engagement and prosocial use occasionally buffer anxiety. Evidence here is primarily correlational and often platform-specific, underscoring the need for more granular longitudinal designs.
Stressful external triggers and their corresponding internal emotional responses amplify PSMU-related anxiety across multiple pathways Dhir et al., 2018; Faelens et al., 2019; Luo & Hu, 2022; Sharma et al., 2023; Alfredson et al., 2024; Anto et al., 2023; Apaolaza et al., 2019; Dodan & Negru-Subritica, 2025; Feng et al., 2025; Hilty et al., 2023; Liu et al., 2024; Prasad et al., 2023; Oppenheimer et al., 2024; Saha et al., 2025; Vagka et al., 2024; Yousef et al., 2025). Stress increases overload, social evaluation threat, and mood dysregulation, while internal emotional responses strengthen reactivity, rumination, and sleep disturbance. High-stress contexts promote behaviours such as procrastination, compulsive posting, and doomscrolling (Dhir et al., 2018; Li & Fan, 2022; Yousef et al., 2025). Although findings consistently support stress/distress as moderators, measurement varies widely across studies, limiting comparability.
Gratification needs, desires for belonging, approval, and self-validation, modulate how users interpret and respond to social media feedback (Dodan & Negru-Subtirica, 2025; Hilty et al., 2023; Dhir et al., 2018; Reich et al., 2024; Jin et al., 2024; Duan et al., 2020). Adaptive strategies like meaningful engagement buffer anxiety, whereas maladaptive strategies intensify evaluative stress and fatigue (Reich et al., 2024). Most available evidence is cross-sectional, and effect sizes vary by age and gender.
Individual susceptibility reflects stable traits such as compulsivity, attentional control, impulsivity, and self-esteem (Dhir et al., 2018; He et al., 2022; Li & Fan, 2022; Mao & Liao, 2025; Qiu et al., 2025; Roberts & David, 2023). Compulsive use consistently predicts anxiety via fatigue and social evaluation threat. Cognitive vulnerabilities (poor attention control, negative bias) link PSMU to anxiety and depressive symptoms. However, most studies rely on convenience samples and self-report measures, indicating a need for multi-method research.
Together, these moderators determine the strength and direction of the mechanisms, resulting in multiple pathways by which PSMU leads to anxiety.
5.2 Mechanistic pathways
The literature converges on a set of mechanisms through which PSMU contributes to anxiety. They encompass pathways involving sleep disruption, social evaluative threat, cognitive overload and fatigue, intolerance of uncertainty and perceived lack of control, and the use of social media for mood regulation and immersive escape. Together, they detail how cognitive, emotional, and behavioural responses to social media evolve, creating reinforcing cycles that heighten vulnerability to anxiety. The following sections outline each mechanism and evaluate the strength and consistency of supporting evidence.
5.2.1 Social Evaluation Threat
Social evaluation threat is one of the most robust pathways through which PSMU contributes to anxiety. Highly evaluative online environments, marked by public metrics, idealised imagery, and continuous opportunities for comparison, heighten users’ sensitivity to judgement and rejection. Fear of negative evaluation, body image concerns, attachment anxiety, contingent self-worth, and low self-esteem collectively increase vigilance and emotional reactivity (Shabahang et al., 2022). Appearance-focused platforms intensify upward comparison and self-objectification (Mills et al., 2018), while fluctuating online feedback fuels insecurity among individuals high in attachment anxiety (Luo & Hu, 2022). Contingent self-worth and strategic self-presentation further weaken resilience to criticism, creating feedback loops that sustain anxiety and reinforce PSMU (Brandao and Denny, 2024). Evidence across experimental, survey, qualitative, and observational designs supports this mechanism, although causal inference is limited. Overall, persistent exposure to evaluative cues fosters chronic self-consciousness, elevating social, appearance-related, and general anxiety, and prompting further checking and rumination (Shabahang et al., 2022; Reich et al., 2024).
PSMU exposes individuals to continuous social comparison, public metrics of approval, and highly evaluative interactions. This environment heightens vigilance about how one is perceived and activates a series of processes, upward comparison, self-presentation pressure, rumination, body image concerns, and sensitivity to feedback, that collectively increase fear of negative evaluation. These effects are intensified on appearance-centric platforms and among individuals with attachment anxiety or low self-esteem (Reich et al., 2024), who become more reactive to perceived rejection (Faelens et al., 2019), fluctuations in likes or comments, and social exclusion. Empirical evidence consistently shows that such evaluative pressures predict appearance anxiety, social anxiety, posting anxiety, and generalised anxiety, particularly when contingent self-worth and feedback-seeking behaviours are involved (Mougharbel et al., 2023; Jiménez-García et al., 2025; Brandao & Denny, 2024; Duan et al., 2020; Agyapong-Opoku et al., 2025; Seabrook et al., 2016; Saleem et al., 2024; Luo & Hu, 2022). Over time, this heightened evaluative focus becomes self-reinforcing, as anxiety fuels more checking, curation, avoidance, and dependence on online feedback, maintaining both PSMU and elevated anxiety levels.
5.2.2 Overload and Fatigue
The overload–fatigue mechanism explains how high-volume, high-intensity engagement produces cognitive burden that translates into anxiety. Compulsive or prolonged PSMU exposes users to continuous information streams and social demands that exceed cognitive capacity, resulting in overload and reduced executive control (Dhir et al., 2018; Li et al., 2023). Overload then gives rise to social media fatigue, mental exhaustion, diminished attentional resources, and reduced emotional tolerance (Dhir et al., 2018; Li et al., 2023). Fatigue triggers downstream outcomes including passive social media use, attentional difficulties, negative attentional bias, and burnout (Weng et al., 2025; Mao & Liao, 2025; (Li & Fan, 2022; He et al., 2022). These consequences each heighten anxiety while simultaneously increasing reliance on social media for distraction or relief, perpetuating the cycle (Feng et al., 2025; Mao & Liao, 2025; Herrell & Foster, 2025; Brandao & Denny, 2024). Overall, fatigue acts as a central psychological hinge connecting excessive exposure to emotional strain and anxiety.
5.2.3 Intolerance of Uncertainty and Perceived Lack of Control
Intolerance of uncertainty and perceived lack of control shape compulsive social media use and related anxiety (Reed & Haas, 2025; Yousef et al., 2025; Weng et al., 2025). Users with low tolerance for uncertainty turn to social media for reassurance, information, or a sense of agency, but this often results in habitual checking, doomscrolling, and repeated monitoring of feeds (Reed & Haas, 2025). These behaviours paradoxically heighten cognitive load, rumination, and fatigue, while reinforcing a diminished sense of control. Evidence shows that compulsive use interacts with intolerance of uncertainty to increase overload and emotional dysregulation across age groups, with younger users particularly vulnerable (Apaolaza et al., 2019; Du et al., 2024; Erliksson et al., 2020). Research on doomscrolling remains mixed, however, exposure to negatively valenced content reliably increases cognitive strain and anxiety (Qiu et al., 2025; Reed & Haas, 2025). This mechanism highlights a self-reinforcing loop in which attempts to reduce uncertainty increase dependency, cognitive burden, and ultimately anxiety.
5.2.4 Mood Regulation and Absorption
Mood regulation and absorption describe how social media functions initially as a coping strategy but eventually contributes to anxiety. Immersive, visually rich platforms promote absorption or temporary distraction from negative affect (Roberts & David, 2023; Alfredson et al., 2024; Angelini & Gini, 2024; Brailovskaia et al., 2018; Roberts & David, 2023). However, prolonged immersion increases rumination, cognitive fatigue, and emotional reactivity, particularly when users repeatedly reflect on social interactions, comparisons, or emotionally salient content (Faelens et al., 2019; Seabrook et al., 2016; Qiu et al., 2025). Rumination, emerging here as a meta-mediator, links absorption to fatigue and appears across multiple PSMU pathways. Fatigue then impairs attentional flexibility and emotion regulation, heightening vulnerability to anxiety (Herrell & Foster, 2025; Faelens et al., 2019; Yousef et al., 2025; Roberts & David, 2023). Moderators such as platform characteristics, contingent self-esteem, FoMO, and co-rumination further intensify this cycle (Roberts & David, 2023; Angelini & Gini, 2024; Fassi et al., 2024; Juel et al., 2025; Faelens et al., 2019; Qiu et al., 2025). Ultimately, while social media offers momentary relief, sustained engagement under distress produces cognitive and emotional strain that exacerbates anxiety over time.
5.2.5 Sleep Disruption
Sleep disruption consistently appears as a meta-mediator linking PSMU with anxiety. It concentrates the effects of upstream vulnerabilities, loneliness, attachment anxiety, stress, negative mood, and attentional dysregulation, and channels them toward downstream emotional difficulties. Evidence from longitudinal and cross-sectional studies shows that mobile dependence, late-night or in-bed use, and heightened rumination impair sleep quality, contributing to insomnia, daytime fatigue, and mood dysregulation (Luo & Hu, 2022; Ahmed et al., 2025; Bhat et al., 2018; Fassi et al., 2024; Herrell & Foster, 2025). These effects are especially salient among adolescents and young adults with pre-existing anxiety or depression. Although supportive online interactions can sometimes improve sleep (Saha et al., 2025), the broader pattern indicates that disrupted sleep both results from PSMU and amplifies subsequent anxiety, interacting with other mechanisms such as social comparison, feedback-seeking, and rumination (Saleem et al., 2024). Sleep therefore functions as a higher-order mediator that intensifies the emotional impact of PSMU across vulnerable groups.
5.3 Life Events as a Meta-Moderator
Life events function as a meta-mediator in the relationship between PSMU and anxiety, encompassing boredom, interpersonal conflict, relocation, academic pressure, and other situational changes. These experiences intensify the psychological impact of PSMU by increasing stress, reinforcing rumination, and heightening cognitive and emotional load, creating bidirectional cycles in which life stressors and social media use mutually amplify vulnerability. Anto et al. (2023) found that students dealing with academic strain, separation from support networks, or appearance-focused content reported greater stress, FoMO, and negative affect. Similarly, Joiner et al. (2013) observed higher internet-related anxiety among first-generation digital natives, suggesting that adaptation to digital contexts interacts with life transitions, while Alfredson et al. (2024) highlight that adolescence and early adulthood, periods marked by rapid change, may heighten sensitivity to social-media-related distress.
Social support moderates these effects. Saha et al. (2025) show that disclosing life events online can improve wellbeing and sleep through supportive responses, whereas limited or negative feedback exacerbates anxiety and rumination. Xie and Wang (2024) further demonstrate that virtual companionship can buffer social anxiety, though excessive reliance on digital interaction may shift anxiety into offline contexts under stress. Overall, life events amplify vulnerability by interacting with proximal mechanisms such as rumination, fatigue, and emotional dysregulation, and these processes reciprocally influence one another, life stressors intensify anxiety, and anxiety heightens sensitivity to stressors, forming a reinforcing loop.
5.4. Anxiety and Feedback Loops
There are multiple dynamic feedback loops between anxiety and PSMU across all mechanisms, which it both shapes and is shaped by social media engagement, rumination, cognitive fatigue, sleep disruption, life events, and dispositional vulnerabilities. Rather than representing an outcome, anxiety becomes part of a reciprocal system: it heightens vigilance to social and informational threats, drives compulsive checking and avoidance, and impairs emotion regulation, thereby amplifying the very mechanisms that contribute to its emergence. In turn, social evaluation threat, overload and fatigue, intolerance of uncertainty and perceived lack of control, mood regulation and absorption, sleep disruption, and life events continually feed back into heightened anxiety. This interconnected network highlights the need for interventions that target not only PSMU but also the broader set of moderators and mediators that sustain these self-reinforcing cycles.
6. Impact
This review advances what appears to be the first comprehensive, mechanism-oriented framework linking PSMU with anxiety. It integrates five core mechanisms: sleep disruption, social evaluation threat, overload-to-fatigue processes, intolerance of uncertainty and perceived lack of control, and mood regulation/absorption. And moderators such as FoMO, loneliness, gratification needs, stress and distress, person susceptibility, and platform use or context, alongside meta-mediators including sleep, rumination, and life events. Moving beyond exposure-based indicators (e.g., duration or frequency of use) toward a relational, feedback-oriented model, the review consolidates previously fragmented findings into a coherent explanatory structure that clarifies how, when, and for whom social media use becomes anxiety-provoking. This provides a clear guide for future longitudinal and experimental research, indicating which constructs should be measured simultaneously and how mediating and moderating effects can be understood.
The proposed model also has direct implications for risk identification, intervention, and platform design. It helps filter high-risk profiles based on dispositional traits, developmental stage, and contextual factors, enabling stratified screening and more precise assessment of problematic use. Simultaneously, it highlights multiple modifiable targets, including sleep disturbance, cognitive overload, rumination, social evaluative pressure, and intolerance of uncertainty, that can be addressed through clinical or non-clinical interventions. Finally, by linking specific platforms to identifiable mechanisms, the framework provides an empirically grounded foundation for safer platform design and regulatory policy, identifying actionable, testable changes that could mitigate social-media–related anxiety at scale.
7. Limitations
This study has several significant limitations that should be considered when interpreting the findings. First, the results are largely correlational, reflecting relational patterns rather than causal pathways; as such, it is not possible to determine directionality, and more research is needed to establish causal links among PSMU, anxiety, and the identified mediating factors. Second, the literature reviewed is heterogeneous, with substantial variation in participant characteristics, baseline anxiety levels, and social media usage patterns, making it difficult to generalise findings across populations. Many of the markers and mechanisms identified, such as rumination, cognitive overload, and intolerance of uncertainty, are highly interconnected, both conceptually and empirically, which complicates efforts to parse them into discrete, independent mechanisms. Similarly, PSMU and anxiety are often tightly intertwined in the literature, further challenging attempts to isolate individual pathways.
The exploratory nature of this study is another limitation. Data extraction was not exhaustive, and the analysis relied on markers that often reflect outcomes rather than upstream causes, making it impossible to construct a fully holistic or definitive model. The mechanisms presented are core features parsed from the literature rather than concrete, individuated steps; they overlap and interact extensively, reflecting the complexity of PSMU rather than clear sequential processes. Conflicting outcomes across studies highlight the need for additional research, particularly studies that investigate individual factors in depth and examine their effects on other components of the model. Overall, the current framework demonstrates possible links and relational patterns between social media use, psychological processes, and anxiety, but it should be viewed as provisional and in need of refinement through more rigorous, targeted research.
Next Steps & Future Directions
Building on the limitations of the current study, several priorities emerge for future research. First, there is a need for longitudinal and experimental designs that can more clearly delineate causal relationships between PSMU, anxiety, and intermediary mechanisms such as rumination, cognitive overload, and intolerance of uncertainty. Research should aim to isolate individual factors and examine their specific contributions while accounting for baseline characteristics, developmental stage, and social context. Comprehensive, multi-method studies, including behavioural tracking, ecological momentary assessment, and self-report measures, would provide richer data to validate and refine the proposed mechanisms.
Another important direction is the development of more precise operational definitions and measurement tools. For instance, clarifying terms like “doomscrolling” and distinguishing between active versus passive engagement, exposure to harmful content, and habitual checking will help reduce inconsistencies and improve comparability across studies. Additionally, moderators such as life events, social support, sleep quality, age, gender and platform characteristics should be systematically examined to understand their role in amplifying or buffering risk.
Beyond research, broader practical implications should be explored. This includes designing interventions that target specific mechanisms, such as social evaluative threat, rumination, cognitive overload, or avoidance, as well as digital literacy initiatives to improve awareness of social media’s psychological effects. Public health strategies could also address structural and social moderators, including promoting sleep hygiene, social support, and adaptive coping strategies among vulnerable populations. Finally, policy-level considerations, such as platform design, algorithm transparency, and content moderation, may help reduce inadvertent reinforcement of anxiety and compulsive engagement.
Overall, future work should aim to move beyond correlational observations, refine mechanistic understanding, and translate findings into evidence-based strategies that mitigate the psychological impact of social media while acknowledging the nuanced and interconnected nature of these mechanisms.
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Acknowledgements
The authors acknowledge the financial support from Wellcome Trust as a part of the MEXA Accelerator program. This support was essential to make this research possible.
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