Share to stop the harm: How social media metrics drive sharing of fact-checking messages via first-person perception

5 citations

Abstract

While fact-checking has received much attention as an important tool to address the prevalence of misinformation, how to ensure fact-checking messages spread as far and wide as misinformation remains to be studied. To fill this gap, this study examined when people decide to share fact-checking messages on social media and what psychological mechanisms underlie such a decision. Two experiments revealed that fact-checking messages debunking a viral misinformation post (i.e. liked, shared, and commented on many times) were perceived to be more socially desirable as compared to fact-checking messages discrediting a non-viral misinformation post. Individuals presumed greater influence of the socially desirable fact-checking messages on themselves than others (i.e. greater first-person perception). Enhanced first-person perception, in turn, led to stronger intentions to share the fact-checking messages on social media. Theoretical and practical implications for fact-checking efforts are discussed.

5
Citations
Research
Paper Only

Study specs

Authors
M Chung
Discipline
Social Science
Year
2024
Human Data Platform
Prolific

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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