发表机构
Tsinghua University; Communication University of China; Tongji University; University of Luxembourg（清华大学; 中国传媒大学; 同济大学; 卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用X平台超2.2亿条评分数据，分析“需要你的帮助”算法提示对社区笔记评分参与的影响，发现其能加速笔记解决并适度提升评分者活跃度，展示了算法助推的潜力。
AI 中文摘要
基于社区的事实核查在对抗错误信息方面具有前景，但其可扩展性受到评分积累缓慢的限制。为应对这一挑战，X等平台实施平台导向的评分机制，具体通过“需要你的帮助”算法提示来针对未解决的笔记。利用X上超过2.2亿条评分贡献的数据集——其中包括190万条平台导向的评分——我们考察了贡献者对笔记提示的响应、笔记的解决情况以及评分者的溢出效应。我们发现：（i）在笔记层面，群体抽样评分集中于具有特定有用性和高度分歧的新近笔记。一旦被抽样，群体抽样评分与更快、更多的向已解决状态的转变相关联。（ii）在评分者层面，在评分者首次观察到群体抽样评分后，评分者表现出显著但适度的每日评分次数、评分速度和标签使用增加，而其他行为无变化。这些结果凸显了算法助推在引导志愿者注意力朝向有争议内容、加速达成共识同时维持评分者参与方面的潜力。
英文摘要
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.
