Going viral: Sharing of misinformation by social media influencers

9 citations

Abstract

This paper tackles the issue of health and well-being misinformation, the dissemination of false or misleading information related to medical treatments, diseases, mental health, nutrition, exercise or general well-being, propagated by social media influencers. It investigates the virality of misinformation posts by TikTok and Instagram influencers exploring users’ appraisals and their sharing tendencies. Grounded in social influence and cognitive appraisal theories (CAT), three online experimental studies dissect the dynamics of virality, user comments and their effects on perceived deception, parasocial interaction and sharing intent. The results of Study 1 demonstrate heightened post virality reduces perceived deception, fostering stronger parasocial connections and sharing intentions. Conversely, lower virality levels heighten deception perceptions. In Study 2, critical comments are shown to enhance deception in high virality posts. Whereas supportive comments are shown to enhance the indirect effect of low virality posts on sharing intentions, via deception and parasocial interaction. The study contributes by demonstrating how social influence theory and CAT together explain how social media influencer misinformation posts based on their virality and user responses are likely to be shared and what consumer appraisals explain this effect. It provides directions of how marketers can tackle this issue.

9
Citations
Research
Paper Only
Relevant for

Study specs

Three online experimental studies grounded in social influence theory and cognitive appraisal theory (CAT), analyzing user behavior in response to influencer posts with varying levels of virality and comment types.

Discipline
Social Science
Study Type
Experimental Study
Year
2025
Human Data Platform
Prolific

Measured Outcomes

Virality of posts, perceived deception, parasocial interaction, sharing intent, and effects of user comments (critical vs. supportive).

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.

1 day ago

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