Platform prompts speed up fact checking and boost volunteer ratings
Needs Your Help: Understanding Platform-Directed Rating Participation in Community Notes on X
Human-Computer Interaction
Summary
Fact-checking online can be slow because it takes time to get enough people to review each claim. This paper looks at how X (formerly Twitter) uses reminders called “Needs Your Help” to ask people to rate unclear fact checks. The authors found these prompts helped people focus on newer and more debated notes, speeding up agreement on facts. Also, after seeing one of these prompts, users tended to rate more often and use tags more. This shows that gentle nudges can help communities check facts faster while keeping volunteers active.
What this means in practice
- •For social media platform teams: Design prompt systems to direct volunteer attention toward unresolved fact-check notes, accelerating their resolution.
- •For online community managers: Implement algorithmic nudges to maintain and increase user fact-checking activity on contested content.
Authors
Shuning Zhang, Changxi Wen, Jiuchang Wang, Dai Shi, Gabriele Lenzini, Yuwei Chuai, Xin Yi
Abstract
Community-based fact-checking is promising in countering misinformation, yet its scalability is constrained by slow rating accumulation. To address this challenge, platforms such as X implement platform-directed rating mechanisms, specifically through ``Needs Your Help'' algorithmic prompts, to target unresolved notes. Using a dataset of over 220 million rating contributions -- including 1.9 million platform-directed ratings -- on X, we examine contributors' response to note prompts, notes' resolution, and raters' spillovers. We found (i) at note level, population-sampled ratings concentrate on recent notes with certain helpfulness and high disagreement. Once sampled, population-sampled rating was associated with faster and more transitions to resolved statuses. (ii) At rater level, following raters' first observed population-sampled rating, raters exhibit significant yet modest increases in daily ratings, rating pace and tag usage, while other behaviors show no change. These highlight the promise of algorithmic nudges to guide volunteer attention toward contested content, accelerating consensus while sustaining rater engagement.