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Community-based fact-checking reduces the spread of misleading posts on social media

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arxiv 2409.08781 v1 pith:PNVCYP7I submitted 2024-09-13 cs.SI

classification cs.SI
keywords communitymisleadingpostsmedianotessocialspreadcommunity-based
verification ladder T0 review T1 audit T2 compute T3 formal
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Community-based fact-checking is a promising approach to verify social media content and correct misleading posts at scale. Yet, causal evidence regarding its effectiveness in reducing the spread of misinformation on social media is missing. Here, we performed a large-scale empirical study to analyze whether community notes reduce the spread of misleading posts on X. Using a Difference-in-Differences design and repost time series data for N=237,677 (community fact-checked) cascades that had been reposted more than 431 million times, we found that exposing users to community notes reduced the spread of misleading posts by, on average, 62.0%. Furthermore, community notes increased the odds that users delete their misleading posts by 103.4%. However, our findings also suggest that community notes might be too slow to intervene in the early (and most viral) stage of the diffusion. Our work offers important implications to enhance the effectiveness of community-based fact-checking approaches on social media.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation

    cs.SI 2025-10 unverdicted novelty 6.0 of 10

    CrowdNotes+ combines LLM note augmentation and automation with a three-stage evaluation to outperform human contributors on correctness, helpfulness, and evidence utility for health misinformation notes.

  2. Commenotes: Synthesizing Organic Comments to Support Community-Based Fact-Checking

    cs.HC 2025-09 conditional novelty 6.0 of 10

    A filtering plus LLM synthesis pipeline turns early user comments into community-note-style fact-checks; the best model beats human notes in 70.1% of pairwise user ratings.

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