Community notes vs. related articles: Assessing real-world integrated counter-rumor features in response to different rumor types on social media
Abstract
The pervasive reach of the Internet has revolutionized information access and transmission, which has contributed to the widespread dissemination of rumors on social media. This study explored the impact of real-world integrated counter-rumor features, specifically community notes (which provide context and additional information from the online community) and related articles (which link to verified news sources that address the rumor), on online users' intentions to believe and spread rumor tweets on social media. Additionally, we investigated how these features mitigate online users' intentions to believe and spread different types of rumor messages, including wish and dread rumors. After conducting an experimental study with 201 online users on social media, we found that the presence of integrated counter-rumor features in rumor tweets can reduce online users' intentions to believe and spread rumors, regardless of the specific feature used. While we observed no significant differences between the effects of community notes and related articles on overall online users' intentions, a nuanced pattern emerged when we considered wish and dread rumors. Specifically, community notes proved more effective at reducing online users' intentions to believe and spread wish-related rumors due to the diverse perspectives and opinions within the online community. By contrast, related articles were found to have greater efficacy at mitigating online users' intentions to believe and spread dread rumors, as they can provide more concrete information to alleviate any associated fear or anxiety. Our findings contribute theoretical and practical insights for effectively countering the spread of rumor tweets on social media platforms.
Study specs
Conducted an experimental study evaluating the effects of community notes and related articles on online users' intentions to believe and spread two types of rumor tweets: wish and dread rumors.
- Institution
- National Cheng Kung University
- Discipline
- Human-Computer Interaction
- Sample Size
- N=201
- Study Type
- Experimental Study
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Measured Outcomes
Online users' intentions to believe and spread rumors on social media with and without integrated counter-rumor features (community notes and related articles).
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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