Did the Roll-Out of Community Notes Reduce Engagement With Misinformation on X/Twitter?
Yuwei Chuai, Haoye Tian, Nicolas Pröllochs, Gabriele Lenzini
Abstract
Developing interventions that successfully reduce engagement with misinformation on social media is challenging. One intervention that has recently gained great attention is X/Twitter's Community Notes (previously known as "Birdwatch"). Community Notes is a crowdsourced fact-checking approach that allows users to write textual notes to inform others about potentially misleading posts on X/Twitter. Yet, empirical evidence regarding its effectiveness in reducing engagement with misinformation on social media is missing. In this paper, we perform a large-scale empirical study to analyze whether the introduction of the Community Notes feature and its roll-out to users in the U. S. and around the world have reduced engagement with misinformation on X/Twitter in terms of retweet volume and likes. We employ Difference-in-Differences (DiD) models and Regression Discontinuity Design (RDD) to analyze a comprehensive dataset consisting of all fact-checking notes and corresponding source tweets since the launch of Community Notes in early 2021. Although we observe a significant increase in the volume of fact-checks carried out via Community Notes, particularly for tweets from verified users with many followers, we find no evidence that the introduction of Community Notes significantly reduced engagement with misleading tweets on X/Twitter. Rather, our findings suggest that Community Notes might be too slow to effectively reduce engagement with misinformation in the early (and most viral) stage of diffusion. Our work emphasizes the importance of evaluating fact-checking interventions in the field and offers important implications to enhance crowdsourced fact-checking strategies on social media.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext efaa7edd-b55e-4c0c-9813-50c5433290aaCited by top-tier papers7
- Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social MediaYuwei Chuai, Anastasia Sergeeva, Gabriele Lenzini, Nicolas PröllochsCHI 2025 · 5 citations
- The Influence of Content Modality on Perceptions of Online MisinformationSuwani Gunasekara, Saumya Pareek, Ryan M. Kelly, Jorge GonçalvesCHI 2025 · 3 citations
- Dialogues with AI Reduce Beliefs in Misinformation but Build No Lasting Discernment SkillsAnku Rani, Valdemar Danry, Paul Pu Liang, Andrew Lippman et al.CHI 2026 · 3 citations
- Beyond Community Notes: A Framework for Understanding and Building Crowdsourced Context Systems for Social MediaTravis Lloyd, Tung Nguyen, Karen Levy, Mor NaamanCHI 2026 · 2 citations
- From TikTok to Telegram: Cross-Platform Efficacy and User Acceptance of Erroneous and Flawless Misinformation InterventionsKatrin Hartwig, Tom Biselli, Franziska Schneider, Immanuel Lamp et al.CHI 2026 · 1 citation
Builds on15
- Birds of a feather don't fact-check each other: Partisanship and the evaluation of news in Twitter's Birdwatch crowdsourced fact-checking programJennifer Allen, Cameron Martel, David G. RandCHI 2022 · 104 citations
- Combating Misinformation in Bangladesh: Roles and Responsibilities as Perceived by Journalists, Fact-checkers, and UsersMd Mahfuzul Haque, Mohammad Yousuf, Ahmed Shatil Alam, Pratyasha Saha et al.CSCW 2020 · 84 citations
- Empirical Analysis of EIP-1559: Transaction Fees, Waiting Times, and Consensus SecurityYulin Liu, Yuxuan Lu, Kartik Nayak, Fan Zhang et al.CCS 2022 · 78 citations
- True or False: Studying the Work Practices of Professional Fact-CheckersNicholas Micallef, Vivienne Armacost, Nasir D. Memon, Sameer PatilCSCW 2022 · 71 citations
- Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert CriteriaMd Momen Bhuiyan, Amy X. Zhang, Connie Moon Sehat, Tanushree MitraCSCW 2020 · 70 citations
Related papers
- Diffusion of Community Fact-Checked Misinformation on TwitterChiara Patricia Drolsbach, Nicolas PröllochsCSCW 2023 · 47 citations
- Request a Note: How the Request Function Shapes X's Community Notes SystemYuwei Chuai, Shuning Zhang, Ziming Wang, Xin Yi et al.CHI 2026 · 1 citation
- Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-CheckingNikil Selvam, Jay Baxter, Sophie Hilgard, Brad Miller et al.ICML 2026
- Beyond the Crowd: LLM-Augmented Community Notes for Governing Health MisinformationJiaying Wu, Zihang Fu, Haonan Wang, Fanxiao Li et al.ACL 2026 · 17 citations
- Reactions to Fact CheckingD. Scott Appling, Amy S. Bruckman, Munmun De ChoudhuryCSCW 2022 · 13 citations
