How Algorithmic Personalization Reshapes Social Comparison and Trust

Why does scrolling Instagram or TikTok so often leave us feeling inadequate, even when we know the content is curated? Over 4.8 billion social media users interact daily with algorithms designed to hold attention, yet a June 2026 study published in *Frontiers in Communication* suggests these same systems fuel social comparison, skew class perception, and erode trust.¹ Li and colleagues show that personalized advertising on platforms like Instagram and TikTok can silently reshape how people assess their own social standing. For communicators and PR professionals, this isn't just a psychological curiosity; it's an ethical and reputational risk that demands a new approach to targeted messaging.

What the New Frontiers in Communication Study Found

A June 2026 study published in *Frontiers in Communication* delivers the first cross-platform evidence that algorithmically personalized advertising does not just sell products: it actively reshapes how we see our place in society and whether we trust our neighbors.¹

The Study at a Glance

Led by researchers from the Communication University of China and Soochow University, the study surveyed users across Douyin, Instagram, and TikTok, using rigorous measurement models, discriminant validity checks, and both indirect and conditional indirect effects analyses. The full design, detailed across five tables and three figures, isolates how algorithmic curation, not just ad content, drives psychological outcomes.

How They Tested Algorithmic Influence

Instead of assuming that personalized ads simply persuade through relevance, the team built a theoretical bridge between two concepts: social comparison theory and algorithmic cognitive mediation. Social comparison theory explains our innate drive to evaluate ourselves against others. Algorithmic cognitive mediation describes the mental steps we take when we realize an app is curating content just for us, steps that can amplify the social comparisons embedded in targeted ads. For communicators interested in the broader landscape, understanding how digital communication vs mass communication frameworks differ is essential context for interpreting these findings.

The Central Finding

The data show that algorithmically personalized advertising significantly influences what the researchers call "class cognition," the perceived social hierarchy a user feels they belong to. More striking, this effect cascades into lower social trust, a generalized wariness toward other people and institutions. In other words, when platforms feed us ads that signal where we stand economically, they do not just mirror inequality; they can deepen our sense of distance and suspicion.

Why Cross-Platform Matters

Most prior work examined a single platform, making it impossible to tell whether outcomes were platform-specific or structural. By comparing Douyin, Instagram, and TikTok, each with distinct algorithmic architectures, this study demonstrates that the pattern holds across different feeds, recommendation systems, and cultural contexts. These dynamics also connect to long-standing concerns about negative effects of mass media on public perception. The implication is clear: the mechanism is not a quirk of one app but a feature of algorithmically driven platform societies.

Read the full peer-reviewed article in Frontiers in Communication.

How Algorithmic Personalization Triggers Social Comparison

Algorithms don't just decide what we see; they reshape how we see ourselves. Drawing on Festinger's 1954 social comparison theory, the recent Li et al. study in *Frontiers in Communication* shows that platform algorithms can systematically trigger upward and downward comparisons by curating a non-random slice of social life.¹ For communicators, understanding this mechanism is critical to managing both audience well-being and brand messaging in algorithmically mediated spaces.

The Basics of Social Comparison

Social comparison theory, introduced by psychologist Leon Festinger, describes an innate human drive to evaluate our own abilities, opinions, and social standing by comparing ourselves to others. Upward comparison occurs when we measure ourselves against individuals we perceive as better off, whether more successful, attractive, or affluent. Downward comparison involves looking to those worse off, sometimes boosting self-esteem. Both processes are natural, but the frequency, intensity, and direction of comparison can be heavily influenced by the environment.

How Algorithms Amplify Comparison

In organic social settings, comparisons arise from diverse, real-world observation. But algorithmic feeds are engineered to maximize engagement. Platforms like TikTok, Instagram, and Douyin surface content based on dwell time, likes, and shares. This reward loop often inadvertently promotes highly aspirational, envy-inducing, or status-oriented material (luxury lifestyles, ideal bodies, extreme achievements) because such content reliably captures attention. The result is a feed that doesn't mirror reality but amplifies skewed social cues. Users are nudged into repeated upward comparisons, often without realizing their field of reference is distorted. Research on the positive and negative effects of mass media has long documented similar distortions in traditional channels, but algorithmic curation intensifies them by orders of magnitude.

From Editorial Gatekeeping to Engagement-Driven Curation

Traditional editorial curation involves human judgment about newsworthiness or audience relevance. Algorithmic curation, in contrast, prioritizes signals of user engagement. There is no explicit intent to trigger social comparison, but the cold optimization for attention can elevate content that sparks emotional responses, particularly envy and aspiration. This distinction matters for communicators: while a magazine editor might balance aspirational content with relatable stories, a purely engagement-driven feed has no such balancing mechanism, potentially deepening social anxiety. Professionals building a marketing communication strategy should account for how their content performs within these amplification loops.

Algorithmic Cognitive Mediation

Li and colleagues frame this process through "algorithmic cognitive mediation," the idea that algorithms don't merely transmit content but actively shape how users think about and evaluate social information.¹ By controlling the salience and frequency of certain social markers (wealth, beauty, status), algorithms alter the baseline for self-comparison. For example, repeated exposure to advertisements featuring high-end products can subtly reinforce perceptions of class hierarchy, even if the user never consciously notices the pattern. This mediation transforms the passive experience of scrolling into a continuous, algorithmically guided exercise in social positioning.

Questions to Ask Yourself

The Link Between Personalized Ads, Class Perception, and Social Trust

Personalized advertising does more than sell products. It silently reshapes how people perceive their place in society and whether they can trust the institutions around them.

The Mechanism of Personalized Ads in Shaping Class Perception

When platforms like Instagram or TikTok serve ads, they don't just target based on interests; they often infer and reinforce a user's socioeconomic status. Someone consistently shown luxury goods or premium services begins to internalize a certain class identity, while another user repeatedly exposed to budget offerings or debt-relief ads may feel locked into a lower stratum. This digital mirroring doesn't just reflect reality. It amplifies perceived class divides by creating a feedback loop where users see only what the algorithm predicts they can afford or aspire to.

From Class Perception to Social Trust Erosion

Perceived class position strongly influences social trust, which includes confidence in neighbors, institutions, and the broader system. When algorithmic personalization continually highlights economic differences, even subtly, it can foster a sense of marginalization among viewers who feel the "system" is tilted against them. Over time, this erodes generalized trust, making individuals more skeptical of media, government, and even interpersonal relationships. The consequences extend well beyond what audiences typically associate with mass media effects. For communicators, the concern is twofold: first, that platform algorithms may inadvertently sow distrust, and second, that brands leveraging hyper-targeted ads may unknowingly contribute to this fragmentation.

Empirical Evidence from Recent Research

Over the past two years, multiple studies have mapped these connections. Analysis of social media feeds reveals that algorithmically curated ad content can heighten social comparison, leading users to perceive greater inequality. Li et al. (2026) found through survey-based modeling that prolonged exposure to class-congruent ads on platforms like Douyin and TikTok reduced participants' trust in public institutions by a measurable margin.¹ While exact mechanisms are still being refined, the consensus is growing: personalized advertising is not just a commercial tool, it is a social force that can recalibrate how entire groups view their community standing.

Implications for a Fragmented Society

This dynamic matters for professionals in communication and public relations because it transforms the ethical landscape of digital outreach. When targeted campaigns amplify perceived class gaps, they risk fueling polarization and reducing the shared social capital necessary for collective action. In a media environment already challenged by misinformation, understanding how to spot fake news becomes just as important as recognizing the subtler distortions algorithmic ads introduce. Strategic communicators must now weigh short-term conversion metrics against long-term societal impact, recognizing that every micro-targeted ad plays a small part in shaping the audience's view of the world. The link between personalized content, class cognition, and trust is no longer theoretical; it is an operational reality that demands reflection and, ultimately, revised practice.

Platform Differences: How TikTok, Instagram, and Douyin Compare

The social media landscape has fractured into distinct algorithmic ecosystems, each curating content in ways that shape user perception and social comparison differently.

The Mechanics of Recommendation: Interest Graphs vs. Social Networks

TikTok's For You Page (FYP) is built on a pure interest graph, clustering content by topic and using optical character recognition to create niche lanes that feel hyper-personalized.¹ The algorithm weights social connections only lightly, so what you see is driven by what you watch, not who you know. Instagram employs a hybrid model: the main Feed and Stories prioritize content from your social graph (friends, family, and accounts you engage with) while Reels and Explore lean into an interest graph similar to TikTok's.² This dual system means Instagram constantly balances identity reinforcement with discovery. Douyin, TikTok's Chinese counterpart, layers on a commerce graph, where purchase history and live-shopping interactions become primary ranking signals.³ Browsing on Douyin is shopping; the platform is designed to turn attention into transaction in real time.

Advertising Logic: From Behavior to Purchase

TikTok's ad targeting uses behaviorally inferred interests and content embeddings from the FYP, applying the same user representations across organic and paid content.¹ This creates a seamless, almost invisible advertising experience that mirrors the user's entertainment feed. Instagram, powered by Meta's data infrastructure, draws on identity, social connections, and off-platform behavior to optimize for conversions, often serving ads that reinforce aspirational identities.² Douyin goes further by integrating e-commerce and local services directly into its recommendation engine, making every live room and video a potential storefront.³ For brands, this means an ad on TikTok feels like entertainment, on Instagram like a lifestyle upgrade, and on Douyin like an immediate purchase opportunity.

What Each Platform Surfaces: Social Comparison Cues

The type of social comparison each platform triggers is rooted in its design. TikTok fosters comparison within aspirational interest niches: users see high-performing lifestyle content and aligned ads that paint a picture of what could be achieved.⁴ Instagram amplifies peer and social-circle comparison, as the algorithm prioritizes content from known people and influencers, making social status a continuous performance.² Douyin intensifies consumerist and local class comparison; scrolling becomes an exercise in benchmarking consumption, where live-shopping hosts and lavish product videos set material standards.³ The study by Li et al. confirms that such platform-specific cues can erode social trust, as users begin to attribute feelings of inadequacy to the algorithmic environment itself rather than to the content they consume. For communicators who want to understand the broader dynamics of communication and mass media, recognizing these platform-level differences is essential.

Why Platform Context Is a Strategic Imperative for Communicators

A communicator cannot rely on a one-size-fits-all message. On TikTok, a brand story might tap into creative self-expression; on Instagram, it must navigate the delicate territory of social status; on Douyin, it should plan for instant commerce and cultural compliance within China's regulatory framework. Professionals weighing a master's in social media marketing versus a broader digital degree will find that platform-specific literacy is increasingly central to the curriculum. Understanding these design differences allows strategic communicators to anticipate how audiences will interpret messaging, not just what they'll see, but how they'll feel about themselves while seeing it. In a platform society where algorithmic personalization reframes social comparison, the ethical communicator aligns brand goals with the psychological safety of the audience.

Related Articles

Self-Esteem, Identity, and Who Is Most Vulnerable

Among young adults who spend three to five hours daily on social media, 82.3% report low self-esteem, and 76.7% experience heightened anxiety immediately after use.¹ These startling numbers reveal a direct line between algorithmically curated feeds and psychological distress. As recent research unpacks the mechanisms behind these trends, a clearer picture emerges: personalized content doesn't just shape what we see, it reshapes how we see ourselves and our place in the social hierarchy.

The Self-Esteem Impact Pathway: From Comparison to Crisis

Algorithmic personalization systematically surfaces content designed to maximize engagement, often amplifying aspirational images, luxury lifestyles, and idealized social comparisons. This constant exposure triggers what psychologists call upward social comparison, where individuals measure themselves against seemingly superior others. The 2026 study by Li and colleagues adds a crucial class-based dimension to this dynamic. Their work shows that algorithmically personalized advertising on platforms like TikTok, Instagram, and Douyin subtly reinforces perceptions of social hierarchy, making users acutely aware of their relative standing. Over time, this chronic upward comparison can lower self-esteem, increase body dissatisfaction, and amplify status anxiety. Visual platforms intensify these effects: 85% of frequent users of visually oriented social media report high anxiety, compared to 55% on text-based platforms.¹ The broader implications for families and media consumers echo longstanding concerns about the positive and negative effects of mass media.

Who Bears the Brunt? Age, Gender, and Platform Effects

Not everyone is equally affected. Age emerges as a powerful moderator. Young adults consistently demonstrate the strongest negative reactions to algorithmic social comparison, with 64.6% reporting recent anxiety symptoms in one 2023-2024 survey.¹ Middle-aged and older users appear less susceptible, though they are not immune.² Gender differences are nuanced. A 2024 study found no overall difference between men and women in their demand for social comparison information, but the nature of that comparison can diverge.³ Women may face greater body-image pressures from visual platforms, while men might encounter status comparisons around wealth or achievement. Socioeconomic status also matters: individuals from lower economic backgrounds who encounter algorithmically targeted luxury ads may experience sharper status anxiety, as Li et al.'s class cognition findings suggest. For communicators, this means that blanket personalization strategies carry disproportionate risk for younger, economically vulnerable segments.

The Long-Term Toll: Anxiety, Depression, and Identity Distortion

Short-term dips in self-esteem are troubling enough, but longitudinal evidence indicates that sustained exposure to algorithmic social comparison can produce deeper mental health consequences. Over months or years, the cumulative effect of repeatedly processing idealized algorithmic content can contribute to chronic anxiety, clinical depression, and a distorted self-identity. Users may begin to internalize the curated lives they see as benchmarks, leading to feelings of inadequacy and hopelessness. While cross-sectional data (e.g., 82.3% low self-esteem) do not establish causation, they align with a growing consensus that algorithmically driven social comparison is not a fleeting discomfort but a persistent mental health stressor. Identity formation, particularly during adolescence and young adulthood, becomes entangled with algorithmic feedback loops that reward conformity to narrow, often unrealistic ideals.

What This Means for Communicators and Campaign Design

Understanding these vulnerability differences is essential for ethical targeting and inclusive campaign design. A one-size-fits-all approach to personalized advertising can inadvertently harm the same audiences communicators aim to reach. For example, a campaign that predominantly showcases unattainable lifestyles can alienate lower-income viewers and erode brand trust. Strategic communicators should prioritize algorithmic literacy, ensuring that messaging does not exploit social comparison tendencies. Professionals looking to sharpen these skills may benefit from exploring how public relations vs marketing vs strategic communication each approach audience-centric design. Designing campaigns that feature diverse, relatable representations and promoting positive, empowering narratives can mitigate harm. Moreover, transparency about algorithmic curation and offering users greater control over their feeds are emerging best practices. As platform societies evolve, communicators hold a critical stake in shaping algorithms that support rather than undermine psychological well-being.

What This Means for Strategic Communicators and PR Professionals

Strategic communicators can no longer view algorithmic ad delivery as a neutral targeting tool; it actively shapes audience perceptions of social class and erodes trust if left unchecked. A 2026 study published in Frontiers in Communication reveals that the context in which a personalized ad appears, surrounded by aspirational or status-driven content, can shift how audiences perceive the brand's own class associations.¹ This isn't just an academic concern. It's a brand reputation risk with real-world consequences.

Audit Your Ad's Algorithmic Neighbors

Begin by examining where your ads surface on platforms like TikTok and Instagram. If your campaign runs alongside content that glorifies wealth, exclusive lifestyles, or unattainable beauty standards, your brand may inadvertently signal exclusivity or elitism. The research shows that such adjacency triggers upward social comparison, making viewers feel less adequate and potentially resentful toward the brand.¹ Conduct a qualitative audit: what does the algorithm's feed look like for your target audience? Map the typical content mix and assess whether your ad's placement reinforces harmful class cues. Even if your creative is inclusive, the surrounding material can undo that intent.

Build Trust Signals Into Creative

The study also links personalized ad exposure to diminished social trust. When audiences feel they are being sorted into narrow categories based on their perceived status, their trust in institutions, including the brands advertising to them, can erode. Counteract this by embedding trust signals directly in your ad creative. Use language that emphasizes transparency ("Why you're seeing this"), community benefit, or collective values over individual aspiration. Highlighting real customer stories, diverse representation, and verifiable claims can mitigate the skepticism that algorithmically primed audiences bring to the screen. Professionals exploring a strategic communications degree will increasingly encounter these ethical considerations in their coursework.

Diversify Your Platform Strategy

Each platform's recommendation engine creates a distinct comparison environment. The study examined Douyin, Instagram, and TikTok, finding that their algorithms curate different social landscapes.¹ A one-size-fits-all campaign ignores these nuances. Diversify not only the platforms you use but also the creative formats and messaging to match the psychological context of each space. For example, on a platform where high-luxury content dominates, consider a counter-narrative that grounds your brand in everyday authenticity. On a platform with more diverse feeds, test messages that emphasize collective goals.

Prioritize Ethical Targeting and Messaging

The ethical implications are stark. The research indicates that vulnerable demographics, those already susceptible to social comparison and class anxiety, are most strongly affected.¹ Communicators must proactively assess whether their targeting criteria and creative content amplify harmful comparison dynamics. Before launching a campaign, ask: could this ad make someone feel more isolated, inadequate, or distrustful? If the answer is even a possible yes, revisit your approach. Public awareness of algorithmic harms is growing, and a brand caught contributing to societal mistrust or class-based anxiety will face reputational fallout. In personalized environments, authenticity and inclusive representation aren't just ethical checkboxes. They are strategic defenses against comparison-driven audience resentment.

Design Interventions and Ethical Frameworks That Actually Work

When Instagram hid public like counts for 154,000 influencer accounts in 2019, starting with a rollout in Canada and expanding to the U.S. later that year, the platform expected a dramatic improvement in user well-being.¹ The reality proved more nuanced. While the test did not produce significant gains in global self-esteem for most users, it did yield small but meaningful reductions in appearance-based comparisons among high-risk subgroups.² These mixed results underscore a central challenge: platform design interventions can shift behavior around the edges, but deeply embedded social comparison patterns resist quick fixes.

Hiding Public Metrics: Modest Gains, Not a Silver Bullet

Instagram's hidden likes experiment remains the most high-profile attempt to reduce social comparison by removing visible engagement counts. According to published results spanning 2020 to 2025, the primary documented effects centered on engagement metrics rather than mental health improvements.² For the average user, hiding likes did not improve overall self-esteem. However, for people already at risk of appearance-related distress, the change offered short-term relief by dampening the immediate trigger of quantifying social approval. From an evidence standpoint, this was a real-world platform rollout with observational data, not a controlled experiment, so causal claims are limited.

Feed Diversification: Breaking the Filter Bubble

A more targeted intervention injects diversity into algorithmic feeds, deliberately exposing users to content outside their established preferences. Research published between 2021 and 2025 shows that diversification algorithms can reduce upward social comparison by interrupting the curated parade of idealized lifestyles and bodies. Studies found a small but statistically significant improvement in body dissatisfaction, though, like hiding likes, global self-esteem showed no consistent change. The evidence here comes primarily from experimental studies (both field tests on platforms and lab simulations), giving it stronger internal validity than the Instagram rollout alone. For communicators interested in the broader psychological dimensions of digital messaging, understanding why empathy matters in audience engagement can complement these technical interventions.

Algorithmic Transparency Labels: Trust Without Comparison Relief

Telling users why they see a particular ad or post ("this was shown to you because…") consistently increases perceived transparency and, when paired with user controls, boosts trust in the platform. Yet the same transparency labels show no strong evidence of reducing social comparison. This finding, documented across multiple studies from 2020 to 2025, suggests that knowing the mechanics of personalization does not neutralize the emotional impact of comparing oneself to the content. Transparency builds institutional trust but does not directly address the psychological mechanisms of comparison.

Friction-Based Nudges: Theory Meets Practice

Proposed but less rigorously tested are friction-based interventions: screen-time reminders, comparison awareness nudges (e.g., "Does this reflect real life?"), and forced pauses before liking. While these align with ethical principles of informed consent and user autonomy, published evidence measuring their effect on social comparison remains scarce. Their promise lies in their low intrusiveness and potential to meet the EU AI Act's transparency and risk-mitigation mandates, which require platforms to limit foreseeable harms.

Ethical Frameworks and the Communicator's Advantage

These interventions are not just platform features; they reshape the environment into which communicators place campaigns. A feed diversification algorithm may limit the reach of hyper-idealized brand imagery; a hidden-like environment may shift success metrics away from public engagement counts. Ethical frameworks like duty of care and informed consent in algorithmic environments challenge brands to anticipate rather than exploit social comparison vulnerabilities. The EU AI Act's emphasis on algorithmic transparency parallels the call for accountability in advertising: communicators who design campaigns that respect user well-being and embrace transparency labels will likely build deeper trust as platforms themselves face regulatory pressure. For strategic communicators, staying ahead of these design pivots, by diversifying content, preparing for engagement metric changes, and aligning with ethical standards, is not reactive defense but proactive leadership in a platform society increasingly aware of its cognitive influence.

Building Algorithmic Literacy as a Core Communication Skill

For many communication professionals, social platforms function as convenient distribution pipelines: post content, gather metrics, repeat. But this mindset treats recommendation engines as neutral conduits, ignoring how algorithmic curation actively shapes the audience's reality. The trade-off is stark: convenience now, or a strategic skill set that separates future-ready communicators from those left guessing.

Reading Platform Algorithm Documentation

Platforms occasionally publish updates explaining how content is ranked, recommended, or suppressed. Learning to parse these documents reveals the signals an algorithm prioritizes, things like watch time, reshare velocity, or comment sentiment. When a platform announces a shift toward "meaningful interactions," for example, a literate communicator immediately tests new content formats rather than sticking with old high-volume tactics. Keeping up with latest trends in communication helps you move from reacting to platform changes to preemptively optimizing your strategy.

Interpreting Engagement Metrics as Algorithmic Artifacts

Likes, shares, and comment counts rarely represent an audience's genuine preferences alone. Each metric is partly a product of what the algorithm chose to amplify. A video that goes viral may surface a highly engaged niche that the algorithm found profitable, not a representative sample of your target demographic. Communicators must triangulate algorithmic metrics with direct audience research (polls, interviews, or purchase data) to separate algorithmic bias from real resonance.

Recognizing Distorted Feedback Loops

Personalized feeds can trap communicators in echo chambers where the content they see, and the responses their messages receive, reflect a narrow slice of reality. When your own feed trains you to expect certain reactions, you risk designing campaigns that miss entire audience segments. Building the habit of auditing your feeds through fresh or logged-out perspectives, and seeking out diverse data sources, guards against these algorithmic illusions.

A/B Testing and Recommendation Logic

Basic fluency in recommendation models, such as collaborative filtering or content-based filtering, helps communicators design smarter experiments. By systematically varying captions, thumbnails, or posting times, you learn what triggers an algorithmic boost. This testing mindset treats platforms as ever-evolving laboratories where strategy is iterated, not guessed. Rigorous A/B testing yields insights far more reliable than chasing trending hashtags.

Looking ahead, regulations like the EU's Digital Services Act and AI Act are compelling platforms to open their black boxes. Communicators who already understand communication and marketing dynamics in algorithmic environments will have a strategic advantage: they can interpret transparency disclosures, adjust to new rules faster, and build campaigns that earn trust because they understand, and respect, the mechanics behind the screen.

Frequently Asked Questions About Algorithmic Personalization and Social Comparison

The June 2026 Frontiers in Communication study by Li and colleagues provides empirical evidence on how algorithmically personalized advertising reshapes social comparison and trust. Below, we address common questions grounded in this research and broader communication scholarship.

How does algorithmic personalization affect social comparison?

Algorithmic personalization curates content that reflects users' perceived interests and social positions, often surfacing material that invites upward or downward comparisons. The Li et al. study shows that personalized ads on platforms like TikTok and Douyin can intensify class-based comparisons by repeatedly exposing users to lifestyle signals tied to socioeconomic status, thereby making social hierarchies more salient.

Can personalized ads change how people perceive social class?

Yes. The study demonstrates that algorithmically targeted advertising influences class cognition by linking products and experiences to specific social strata. Repeated exposure to luxury or aspirational ads can shift a viewer's sense of where they stand in society, reinforcing or altering class perceptions through the lens of social comparison theory.

Does algorithmic personalization reduce social trust?

The research indicates that algorithmically driven social comparisons can erode social trust. When users constantly see content that highlights inequality or idealized lifestyles, their trust in institutions and other groups may decline. This effect is mediated by how individuals cognitively process these comparisons within the platform society.

What is the relationship between social comparison theory and social media algorithms?

Social media algorithms operationalize social comparison by determining which peers, ads, and content users encounter. The Li et al. study integrates algorithmic cognitive mediation, suggesting that algorithms structure the comparison environment, influencing who or what we compare ourselves to, and thus magnifying the emotional and perceptual outcomes predicted by classic social comparison theory.

How can communicators mitigate negative effects of algorithmic personalization?

Communicators can advocate for algorithmic transparency, diversify content feeds to break filter bubbles, and design campaigns that promote inclusive messaging. The study implies that strategic communicators should audit targeted ads for unintended class signaling and consider ethical frameworks that prioritize societal well-being over narrow persuasion metrics.

What platforms are most associated with algorithmic social comparison?

The study specifically examines Douyin, Instagram, and TikTok, all of which rely on sophisticated recommendation engines. These platforms are particularly prone to eliciting social comparison because their algorithms continuously personalize short-form video content, making class and status distinctions immediately visible and emotionally charged.