Creator Trustworthiness Signals: What to Track Before You Partner

Screening

We screened in sources based on their abstracts that met these criteria:

• Digital Platform Context: Does this study examine social media platforms or digital content platforms where creator-audience relationships develop?

• Trustworthiness Outcome: Does this study measure or assess perceived trustworthiness, credibility, or reliability of content creators, influencers, or social media users?

• Platform Signals and Relationships: Does this study analyze observable behavioral or engagement metrics on social platforms AND establish or test relationships between these platform signals and trustworthiness perceptions?

• Human Participants: Does this study involve human participants as evaluators or audiences making trustworthiness assessments?

• Study Design: Is this an empirical study (experimental, quasi-experimental, cross-sectional, longitudinal, or observational design) or a systematic review/meta-analysis with empirical data?

• Creator Focus: Does this study focus on creator trustworthiness rather than solely on information content trustworthiness or accuracy?

• Behavioral Signals: Does this study include behavioral or engagement signals rather than focusing exclusively on static demographic or profile characteristics?

• Generalizability: Does this study provide broader generalizability beyond single-creator case studies and include empirical data rather than being purely theoretical?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

TLDR: The Behavioral Proxy Approach to Creator Trustworthiness

1. The Paradigm Shift: From Metrics to Behavioral Proxies

In the mature creator economy, traditional Key Performance Indicators (KPIs) such as likes, shares, and raw engagement rates have become noisy variables. From a behavioral science perspective, these metrics represent downstream consequences. They are the secondary effects of an audience’s pre-existing trust, not the upstream predictors of it.

The data supports a shift away from monitoring lagging indicators and toward identifying behavioral proxies. These are observable signals that reveal the underlying structural integrity of a creator’s influence.

Relying on traditional engagement metrics creates “reputation-burning” partnerships. In this dynamic, a creator spends down their social capital to fulfill sponsorship obligations, which drives long-term decay of both creator equity and brand equity. A proxy-based approach mitigates this risk by prioritizing source credibility markers that persist beyond a single campaign.

High engagement on a sponsored post can mask an authenticity crisis. When strategists optimize for raw reach and surface-level metrics, they risk entering an adverse selection loop where they partner with creators who are effectively cashing out their reputation. By prioritizing leading proxies, high-value brands can select creators whose credibility ensures the message is internalized, not just viewed.

2. The Universal Signal: Analyzing the Sponsored-Organic Performance Gap

The most consistent quantifiable indicator of creator trustworthiness across digital ecosystems is the sponsored-organic performance gap. This delta captures the “reputation-burning effect” that emerges when commercial content reduces perceived authenticity.

Evidence across YouTube and Bilibili shows that sponsored videos typically cost influencers between0.17% and 0.19% of their subscriber basecompared to equivalent organic content.

This gap provides a direct diagnostic window into the Persuasion Knowledge Model. A high delta signals that the audience has interpreted the content as a commercial disruption rather than a value contribution, triggering defensive processing.

The performance gap is not inevitable. Strategists can reduce the reputation-burning effect through two control levers:

1. Brand-influencer congruence
   Align the sponsorship with the creator’s usual thematic material.

2. Brand selection
   Promoting less well-known brands can reduce the performance gap, since audiences may interpret the partnership as authentic discovery rather than pure commercialization.

In addition,face presence, particularly in video formats, operates as a meaningful moderator that softens negative instantaneous audience response such as live comments and sentiment.

3. Segmented Vetting: Micro-Influencer vs. Macro-Influencer Trust Signals

Vetting protocols must be calibrated to creator audience size. The psychological filters applied by audiences change as a creator scales, reflected in the “authenticity premium” being materially stronger for micro-influencers (β = 0.63) than for mega-influencers (β = 0.41).

Misapplying these signals is a frequent strategic failure.

• High-arousal enthusiastic language increases trust for micro-influencers but reduces impact for macro-influencers (IRR = .944).

• For large creators, intense enthusiasm is often decoded as commercially motivated exaggeration.

For macro-influencers, the data supports a stronger focus onargument qualityandcounterbalanced valence, meaning the creator explicitly addresses both strengths and limitations. This stabilizes credibility and reduces perceived manipulation.

4. Environmental Conditioning: Platform Architecture and Content Format

Trustworthiness is not a static trait. It is conditioned by platform affordances, meaning the mechanisms through which each platform delivers and ranks content. The audience mindset differs between search-oriented and scroll-oriented environments, which shifts the credibility filter applied to creator signals.

• Scroll-driven platforms (TikTok, Instagram)
  Users operate in an experiential mindset where positive sentiment drives follow behavior. Sociable language can mitigate the platform preference for positivity, enabling more nuance without eroding likability.

• Search-driven platforms (Yelp, Goodreads)
  Users operate in a goal-directed mindset. Mixed sentiment becomes the primary credibility marker, while overly positive reviews can be discounted as inauthentic.

• Live-streaming environments (Taobao Live, Twitch)
  Relational signals dominate competence signals. Perceived interaction and homophily, meaning perceived similarity, become the primary trust proxies (β = 0.176).

Live-streaming contains a major execution trap. Homophily negatively moderates the expertise-trust relationship (β = −0.137). This means that in high-relatability contexts, an overly formal expert posture can reduce trust.

Strategists should also evaluate companion presence, such as others appearing in-frame or visible social context cues. These increase perceived humanness and significantly boost trustworthiness (IRR = 1.699).

5. The Disclosure Framework: Managing the Transparency Paradox

The transparency paradox describes the tension between two proven mechanisms:

• Persuasion Knowledge Model explains immediate negative reactions to explicit disclosures such as “#ad”

• Source credibility explains the long-term value of transparency in sustaining trust

Disclosure therefore has a dual time-horizon effect:

• Immediate effects
  Disclosed ads may experience lower instantaneous engagement.

• Persistent effects
  Transparent disclosure produces positive persistent effects on future engagement, while undisclosed advertising creates long-term credibility decay.

Strategic Implication
Disclosure management must be segmented by audience usage intensity.

• Heavy platform users respond positively to clear “#ad” disclosures (Δ = 0.443) because transparency increases perceived integrity.

• Light users often do not register disclosure signals and show minimal response.

In addition, platform-initiated branded content tools tend to generate stronger negative effects (−0.254) than creator-authored in-text disclosures. The recommended approach is to use both. This maintains compliance while allowing the creator’s own wording to soften the standardized platform tag.

6. Implementation: The Behavioral Vetting Protocol

To move from measuring reach to managing reputation, a multi-layered behavioral vetting protocol should be applied.

1. Longevity and content consistency audit
   Evaluate account registration age (η² = 0.14 for credibility) and thematic consistency. Consistency in topic signature over time is a proven credibility predictor.

2. Linguistic arousal calibration
   Ensure the creator’s tone matches their audience scale. High arousal is effective for micro-influencers; informational framing and controlled language is more effective for macro-influencers.

3. Persistence and sincerity check
   Review historical audience feedback dynamics. Creators who disable comments are perceived as less receptive to consumer voice and therefore less sincere.

4. Network oversight and status verification
   For expertise-led domains, confirm external validation signals such as contributor publication experience and ownership status in specialized networks.

Satisficing theory suggests audiences do not require a perfect score across all trust signals. A brand does not need to optimize every proxy simultaneously.

Instead, identify one “hero signal” that anchors credibility, such as:

• strong longevity track record

• consistently low sponsored-organic performance gap

• exceptional argument quality

A deep audit for one high-impact anchor is strategically superior to a broad but shallow checklist approach.

Our take

The transition from engagement-based metrics to behavioral proxies is a shift from reactive monitoring to proactive reputation management. By prioritizing the true signals of trust, including the sponsored-organic performance gap, segment-specific linguistic cues, and platform-conditioned sentiment dynamics, brands can build creator partnerships that persist beyond the current algorithm cycle.

Characteristics of Included Studies

This systematic review synthesizes findings from 40 studies examining observable social-platform signals as proxies for creator trustworthiness. The included studies span multiple platforms, methodological approaches, and geographic contexts. See appendix

The included studies demonstrate methodological diversity, with 15 purely observational designs, 9 experimental studies, and 10 mixed-methods approaches. Instagram was the most frequently studied platform (14 studies), followed by YouTube (8 studies) and TikTok (4 studies). Several studies examined enterprise or specialized platforms such as social trading platforms and investment advice platforms. Geographic coverage includes North America, Europe, and Asia, though many studies did not specify location. Full texts were available for 16 of the 40 studies, with the remainder analyzed based on abstracts only.

Engagement Metrics as Trust Proxies

Multiple studies examined traditional engagement metrics (likes, comments, shares) as signals of trustworthiness, though findings reveal these metrics serve more as consequences than proxies of trust. Zhang et al. (2023) found that engagement metrics including likes and comments differed significantly between sponsored and organic content, with sponsored videos generating weaker audience responses. Similarly, Cheng et al. (2024) observed that audience response gaps in likes, comments, and comment texts were larger among influencers with larger audiences, suggesting engagement patterns signal authenticity concerns rather than directly predicting trustworthiness.

Cascio Rizzo et al. (2023) demonstrated that engagement serves as a downstream indicator of trustworthiness rather than an antecedent, with high-arousal language increasing engagement for micro-influencers (IRR = 1.036, p < .001) but decreasing engagement for macro-influencers (IRR = .944, p < .001). This pattern suggests that engagement metrics must be interpreted in light of audience size and content characteristics.

Content Characteristics and Language Signals

Content-based signals emerged as among the strongest predictors of perceived trustworthiness across studies. Argument quality was identified as a particularly important signal, especially for larger influencers. Pozharliev et al. (2022) found that meso-influencers (10,000 to 1 million followers) were perceived as credible sources only when their posts provided strong argument quality, with trustworthiness measured using Ohanian's scale achieving α = 0.95.

Han et al. (2018) identified specific linguistic features predictive of author credibility on Twitter: positive associations with account registration age, tweets similarity, sentiments in tweets, formal language markers (articles, prepositions, six-letter words), with effect sizes measured through η² values (e.g., η² = 0.14 for account registration age). Negative associations were found for swear words, first-person singular pronouns, questions, and negations.

Aggregate sentiment emerged as a cross-platform indicator of credibility. Shalev et al. (2025) found that aggregate communicator sentiment across multiple posts differentially affected following decisions depending on platform type: positive sentiment drove following on scroll-driven platforms (Twitter/X, Instagram), while mixed sentiment signaled credibility on search-driven platforms (Yelp, Goodreads).

Sponsored Content Disclosure as a Trust Signal

The sponsored-organic performance gap emerged as one of the most robust and quantifiable signals related to trustworthiness. Three studies using difference-in-differences designs on YouTube data consistently found a "reputation-burning effect" from sponsored content.

Disclosure practices themselves serve as signals with complex effects. Karagür et al. (2021) found that platform-initiated branded content tools had stronger negative effects on perceived trustworthiness compared to self-generated in-text disclosures, with significant indirect effects (−0.254 [−0.486; −0.089]). However, Saternus et al. (2022) found that "#ad" disclosures actually increased trustworthiness for heavy Instagram users (Δ = 0.443, p < 0.05), though not for light users. Waltenrath et al. (2024) reconciled these findings by showing that while disclosed ads gathered less immediate engagement than undisclosed ads, they produced positive persistent effects on future engagement, whereas undisclosed advertising produced negative persistent effects.

Dzreke et al. (2025) found clear sponsorship disclosures (e.g., #Ad, Paid Partnership tags) correlated positively with perceived authenticity (r = 0.42, p < .001) and engagement (r = 0.35, p < .001), supporting the notion that transparency signals function as trust proxies.

Creator-Audience Interaction Signals

Responsiveness and interaction quality emerged as significant trust predictors, particularly in social commerce contexts. Senali et al. (2024) found that responsiveness positively influenced trust in sellers on Instagram, while review quantity and review quality both served as positive predictors of trust.

Daniels et al. (2024) found that influencers who disabled social media comments were perceived as less receptive to consumer voice and thus less sincere, leading to more negative impressions and reduced persuasiveness. This effect was mitigated when consumers believed self-protection was a reasonable priority.

In live-streaming contexts, Cao et al. (2025) found that perceived interaction positively predicted trust, with this relationship strengthened by homophily (β = 0.176, p < .01). Notably, homophily negatively moderated the expertise-trust relationship (β = −0.137, p < .05), suggesting that when audiences perceive similarity with streamers, interaction signals become more important than expertise signals.

Visual and Authenticity Markers

Visual signals related to creator presence demonstrated significant effects on trustworthiness perceptions. Ma et al. (2023) found that influencers showing their faces during advertisements moderated the negative effects on instantaneous audience response, with significant changes in live comments and sentiment (p < 0.001).

Cascio Rizzo et al. (2023a) examined virtual influencers and found that companion presence in photos significantly boosted perceived trustworthiness (IRR = 1.699, SE = .068, t = 13.24, p < .001). This effect operated through increased perceptions of humanness, which enhanced trust.

Brand-influencer congruence served as a consistent authenticity signal. Cheng et al. (2022, 2024) found that high fit between sponsored content and an influencer's "usual" content mitigated the reputation-burning effect. Similarly, promoting less well-known brands reduced negative effects on trustworthiness.

Expertise and Source Credibility Signals

Expertise demonstration emerged as a strong trust predictor across multiple contexts. Van Canh Vu et al. (2024) found expertise to be the strongest predictor of trustworthiness among source credibility dimensions (β = .36, p < .001), followed by attitude homophily (β = .20, p < .001), social attractiveness (β = .19, p < .001), and physical attractiveness (β = .09, p < .05).

In investment contexts, Wang et al. (2014) found that a subset of "top authors" on SeekingAlpha contributed content showing significantly higher correlation with future stock performance, and these authors could be identified through user interactions with their articles without requiring historical market data. This suggests that audience engagement patterns can serve as proxies for expertise-based trustworthiness.

Elliott et al. (2018) found that network oversight, contributor publication experience, and contributor ownership status all served as positive credibility cues for investors. Importantly, the presence of one positive credibility cue was sufficient to increase investor perceptions, with additional cues having little incremental influence—a pattern consistent with satisficing theory.

Behavioral Consistency Signals

Account longevity and behavioral consistency emerged as trust signals in several studies. Han et al. (2018) found account registration age to be among the strongest predictors of perceived credibility on Twitter, with η² = 0.14. Tweet similarity (consistency in content themes) also showed positive associations with credibility perceptions.

Wilczyński et al. (2025) found that frequent information-seeking from influencers (OR = 3.54, 95% CI 2.45–5.22) was the strongest predictor of trust among physiotherapy students, followed by perceiving influencers as more informative than academic staff (OR = 2.00, 95% CI 1.46–2.76) and intensive Instagram use (OR = 1.41, 95% CI 1.06–1.87). Age, study year, and prior critical-appraisal training were not significant predictors.

Followership status demonstrated particularly strong effects. Saternus et al. (2022) found that being a follower of an influencer strongly improved trustworthiness perceptions (Δ = 1.909 for heavy users, Δ = 1.861 for light users, p < 0.01), making it the strongest observed proxy in their study compared to disclosure type effects.

Synthesis

The heterogeneity in findings across studies can be explained through several key distinctions related to influencer characteristics, content context, and audience factors.

Influencer Size as a Critical Moderator

The most consistent pattern across studies is that the relationship between social-platform signals and trustworthiness depends fundamentally on influencer audience size. Studies consistently show that micro-influencers and macro-influencers operate under different signal-trust dynamics.

For micro-influencers, high-arousal language increases perceived trustworthiness and engagement (IRR = 1.036, p < .001), while the same language decreases trustworthiness for macro-influencers (IRR = .944, p < .001). This pattern can be explained mechanistically: micro-influencers maintain closer parasocial relationships with smaller audiences who interpret enthusiasm as authentic excitement, whereas larger audiences of macro-influencers interpret the same signals as commercially motivated exaggeration.

Similarly, the reputation-burning effect from sponsored content is significantly stronger among influencers with larger audiences. Zhang et al. (2023) found that smaller audiences form stronger ties and are more receptive to sponsored content, while Steils et al. (2022) found that disclosure messages improve engagement for macro-influencers only when published by the influencer rather than the platform.

Dzreke et al. (2025) quantified this difference, finding that micro-influencers showed a stronger authenticity premium (βauthenticity = 0.63) compared to mega-influencers (βauthenticity = 0.41). This suggests that for micro-influencers, authenticity signals (relatability, genuine enthusiasm) are the primary trust proxies, while for macro-influencers, expertise signals and transparent disclosure become more important.

Platform Type Conditions Signal Effectiveness

The effectiveness of specific signals varies by platform architecture and user goals. Shalev et al. (2025) demonstrated that search-driven platforms (Yelp, Goodreads) favor credibility-signaling behaviors, where mixed sentiment serves as a trust proxy, while scroll-driven platforms (Twitter/X, Instagram) favor likability-signaling behaviors, where positive sentiment drives following. This distinction reflects different user orientations: goal-directed information seeking versus experiential content consumption.

Alternative signals can substitute for primary trust cues depending on platform context. On scroll-driven platforms, sociable language mitigates the preference for positive sentiment. On search-driven platforms, formal credibility badges (e.g., Yelp's "Elite" status) reduce reliance on mixed sentiment as a credibility signal.

In live-streaming contexts, real-time interaction signals become paramount. Cao et al. (2025) found that perceived interaction's effect on trust was strengthened by homophily (β = 0.176, p < .01), while expertise's effect was weakened (β = −0.137, p < .05). This suggests that in synchronous, interactive contexts, relational signals supersede competence signals as trust proxies.

Content Context and Commercial Intent

The commercial context of content fundamentally shapes signal-trust relationships. Multiple studies demonstrate that sponsored content triggers persuasion knowledge activation, altering how audiences interpret signals.

Waltenrath et al. (2024) reconciled seemingly contradictory findings about disclosure effects by distinguishing immediate from persistent effects. Disclosed advertising gathers less immediate engagement than undisclosed advertising, consistent with persuasion knowledge activation. However, transparent disclosure produces positive persistent effects on future engagement, while undisclosed advertising produces negative persistent effects. This suggests that source credibility explains long-term effects, while the Persuasion Knowledge Model explains immediate responses.

Brand characteristics also moderate signal effectiveness. Less well-known brands mitigate the reputation-burning effect, and Walsh et al. (2024) found that for popular creators, sponsorship can enhance consumer engagement when the endorsed brand is perceived as small. This suggests that audiences interpret endorsements of smaller brands as more authentic choices rather than purely commercial arrangements.

Audience Characteristics and Usage Intensity

Audience characteristics condition how signals are interpreted. Saternus et al. (2022) found that heavy Instagram users (ten or more hours per week) responded positively to "#ad" disclosures (Δ = 0.443, p < 0.05), while light users showed no significant response. This suggests that experienced users value transparency signals that less experienced users may not notice or prioritize.

Trust disposition negatively moderates signal-trust relationships. Senali et al. (2024) found that trust disposition negatively moderated the impact of review quality on trust in sellers and responsiveness on trust in products. High-trust individuals may rely less on external signals because they approach interactions with favorable baseline assumptions.

Age demonstrates an inverse correlation with susceptibility to influencer persuasion, though Wilczyński et al. (2025) found that age was not a significant predictor of trust in physiotherapy influencers among students. This apparent contradiction may reflect that age effects operate through different mechanisms in professional versus consumer contexts.

Strongest Signal Proxies by Context

Based on the synthesis of effect sizes and consistency across studies, the following hierarchy of signal strength emerges:

For micro-influencers: Followership status (Δ = 1.86–1.91, p < 0.01), high-arousal language (IRR = 1.036), authenticity cues (r = 0.42 for clear disclosures), and relational signals (parasocial interaction, responsiveness).

For macro-influencers: Expertise demonstration (β = .36, p < .001), transparent disclosure (positive persistent effects), informative framing (1.8% engagement increase), argument quality (significant EEG and self-report effects), and brand-influencer congruence.

For live-streaming contexts: Perceived interaction (β = 0.176 with homophily), interactivity features, face presence (p < 0.001 for sentiment moderation).

For investment/expertise domains: Account longevity (η² = 0.14), content consistency, network status, publication experience, and user interaction patterns with content.

The sponsored-organic performance gap serves as a universal, quantifiable signal across contexts, with consistent effect sizes of 0.17–0.19% reputation cost per sponsored video on YouTube, though this effect is moderated by audience size, content fit, and brand characteristics.

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Appendix

Table 1.1