# Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking

arXiv: https://arxiv.org/abs/2607.01824 · HTML: https://mlsanity.com/paper/2607.01824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking](https://mlsanity.com/paper/2411.06116/) (2024)
- [Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governance](https://mlsanity.com/paper/2301.09976/) (2023)
- [Adversarial Robustness for Tabular Data through Cost and Utility Awareness](https://mlsanity.com/paper/2208.13058/) (2022)
- [Challenges in Trustworthy Human Evaluation of Chatbots](https://mlsanity.com/paper/2412.04363/) (2024)
- [Community notes reduce engagement with and diffusion of false information online](https://mlsanity.com/paper/2502.13322/) (2025)
- [Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation](https://mlsanity.com/paper/2510.11423/) (2025)
- [Can Community Notes Replace Professional Fact-Checkers?](https://mlsanity.com/paper/2502.14132/) (2025)
- [Scaling Human Judgment in Community Notes with LLMs](https://mlsanity.com/paper/2506.24118/) (2025)
- [Community-based fact-checking reduces the spread of misleading posts on social media](https://mlsanity.com/paper/2409.08781/) (2024)

## Abstract (arXiv)

Crowdsourced fact-checking systems have been adopted by major social media companies such as X, Meta, TikTok and Google with the aim of combating misleading information at scale without relying on centralized editorial control. These systems have been developed around a common underlying concept: a bridging mechanism that identifies notes flagging misleading information when they receive support from people with different perspectives rather than simple majority support. To our knowledge the only publicly disclosed bridging algorithms deployed for fact-checking are based on matrix factorization, as deployed by both X and Meta, augmented with additional components addressing abuse, targeted manipulation, and contributor brigades. This work examines the core matrix factorization portion of these systems, presenting theoretical and empirical evaluations of the degree to which coordinated users could vote strategically by leveraging the latent representations to fabricate the appearance of synthetic consensus within the bridging mechanism. Using historic production data, we find that up to 10.7% of lower quality notes could be manipulated above consensus thresholds using less than 10 ratings. We complement these findings with a theoretical analysis, revealing counterintuitively that rating a note as "Not Helpful" can increase its helpfulness score, as well as a cost model quantifying manipulation effort. We have developed and deployed mitigations within X's Community Notes algorithm to address synthetic consensus.