# Truth with a Twist: The Rhetoric of Persuasion in Professional vs. Community-Authored Fact-Checks

arXiv: https://arxiv.org/abs/2601.14105 · HTML: https://mlsanity.com/paper/2601.14105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation](https://mlsanity.com/paper/2210.15723/) (2022)
- [Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governance](https://mlsanity.com/paper/2301.09976/) (2023)
- [Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals](https://mlsanity.com/paper/2309.07601/) (2023)
- [EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles](https://mlsanity.com/paper/2406.12614/) (2024)
- [Diffusion of Community Fact-Checked Misinformation on Twitter](https://mlsanity.com/paper/2205.13673/) (2022)
- [Community-based fact-checking reduces the spread of misleading posts on social media](https://mlsanity.com/paper/2409.08781/) (2024)
- [Who Checks the Checkers? Exploring Source Credibility in Twitter's Community Notes](https://mlsanity.com/paper/2406.12444/) (2024)
- [SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification](https://mlsanity.com/paper/2303.09421/) (2023)
- [Community Moderation and the New Epistemology of Fact Checking on Social Media](https://mlsanity.com/paper/2505.20067/) (2025)
- [Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social Media](https://mlsanity.com/paper/2409.08829/) (2024)
- [Timeliness, Consensus, and Composition of the Crowd: Community Notes on X](https://mlsanity.com/paper/2510.12559/) (2025)

## Abstract (arXiv)

This study presents the first large-scale comparison of persuasion techniques present in crowd- versus professionally-written debunks. Using extensive datasets from Community Notes (CNs), EUvsDisinfo, and the Database of Known Fakes (DBKF), we quantify the prevalence and types of persuasion techniques across these fact-checking ecosystems. Contrary to prior hypothesis that community-produced debunks rely more heavily on subjective or persuasive wording, we find no evidence that CNs contain a higher average number of persuasion techniques than professional fact-checks. We additionally identify systematic rhetorical differences between CNs and professional debunking efforts, reflecting differences in institutional norms and topical coverage. Finally, we examine how the crowd evaluates persuasive language in CNs and show that, although notes with more persuasive elements receive slightly higher overall helpfulness ratings, crowd raters are effective at penalising the use of particular problematic rhetorical means