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Related Experiment Video

Updated: May 7, 2026

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Community-based fact-checking reduces the spread of misleading posts on X (formerly Twitter).

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Community notes significantly reduce the spread of misleading posts on X (formerly Twitter) by 61.2%. While effective, these interventions often occur too late to impact initial viral spread, leading to a modest 14.9% reduction in overall engagement.

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Area of Science:

  • Social Media Studies
  • Information Science
  • Computational Social Science

Background:

  • Misinformation on social media poses a significant societal challenge.
  • Community-based fact-checking initiatives aim to mitigate the spread of false content.
  • Empirical evidence on the causal impact of community fact-checking on social media is limited.

Purpose of the Study:

  • To empirically assess the effectiveness of community notes in reducing the spread of misleading posts on X (formerly Twitter).
  • To quantify the impact of community notes on user behavior, including post deletion.
  • To evaluate the system-wide effect of community notes on overall engagement with misinformation.

Main Methods:

  • Large-scale empirical study utilizing a Difference-in-Differences design.
  • Analysis of repost time series data for 237,180 community fact-checked cascades.
  • Statistical analysis to measure the reduction in spread and increase in deletion rates.

Main Results:

  • Community notes reduced the subsequent spread of misleading posts by an average of 61.2%.
  • The intervention increased the likelihood of users deleting their misleading posts by 94.3%.
  • The system-wide reduction in total engagement with misleading posts was 14.9% due to late intervention.

Conclusions:

  • Community notes are effective in curbing the spread of misinformation once applied, but often too late to stop initial viral diffusion.
  • While impactful on individual posts, the overall system-wide effect on engagement is more modest.
  • Findings offer insights for enhancing the timeliness and effectiveness of community-based fact-checking on social media platforms.