Share to stop the harm: How social media metrics drive sharing of fact-checking messages via first-person perception
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.
Study specs
- Authors
- M Chung
- Institution
- Northeastern University
- Discipline
- Social Science
- Year
- 2024
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
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.
Non-naive Participants Issue
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.
RLHF Applicability to This Study Design
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.
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