发表机构
Georgia Institute of Technology; Arizona State University（佐治亚理工学院; 亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过X平台开源算法，发现参与度可互换性机制使虚假信息因即时反应获得更多推荐而传播更快，并提出反思阈值门控修复方案，在46项检验中显著缩小可信度曝光差距且不损害主流曝光。
AI 中文摘要
虚假信息被广泛报道在基于参与度的平台上传播更快，然而以往的研究主要集中于实证分析，并未识别出导致这一现象的具体算法机制。得益于X平台推荐算法的开源，我们进行了我们所知的首次针对社交媒体平台部署的推荐算法的组件级研究，该研究考察了其每个组件如何影响虚假信息的传播。具体而言，我们识别出算法中的参与度可互换性机制，其中最终推荐分数被构建为所有预测用户活动的加权和。因此，一条推文可能仅仅因为被预测会引发大量即时反应（如点赞和转发）而被反复推荐，即使它预计不会引发深思熟虑的回应（如回复和引用）。由于虚假信息通常从即时反应中获取更大份额的参与度，这一机制使其能够获得更多推荐曝光并更快传播。为了实证验证这一机制，我们在USC X 2024选举语料库上重新实现了X的推荐算法，并构建了一个校准模拟研究来分析不同评分规则的影响。我们发现，重新调整指标权重对减少可信度曝光差距几乎没有影响，甚至产生负面影响，而那些为放大设定深思熟虑参与前提的评分规则能够在46项稳健性检验中显著缓解这一差距。因此，我们的诊断产生了一个简单且可部署的修复方案，即一个反思阈值门控，该门控在推文被预测会引发深思熟虑的参与之前暂缓放大，我们发现这能够将曝光从低可信度内容重新分配出去，且不损害主流曝光，也不损失参与度。
英文摘要
Misinformation is widely reported to propagate faster on engagement-based platforms, yet prior work largely focused on empirical analysis, without identifying a specific algorithmic mechanism that results in this phenomenon. Thanks to the open-sourcing of X's recommendation algorithms, we conduct what is, to our knowledge, the first component-level study of the recommendation algorithm deployed by a social media platform, which examines how each of its components affects misinformation propagation. Specifically, we identify the engagement fungibility mechanism in the algorithm, where the final recommendation score is constructed as a weighted sum of all predicted user activities. As a result, a tweet can be repeatedly recommended simply because it is predicted to draw many instant reactions (e.g., likes and retweets), even when it is not expected to draw thoughtful responses (e.g., replies and quotes). Since misinformation typically draws a larger share of its engagement from instant reactions, this mechanism enables it to receive more recommendation exposure and to propagate faster. To empirically validate this mechanism, we re-implement X's recommendation algorithm on the USC X 2024 election corpus, and build a calibrated simulation study to analyze the impact of different scoring rules. We find that re-tuning the metric weights has little or even a negative impact on reducing the credibility exposure gap, while those scoring rules that set a precondition of thoughtful engagement for amplification would be able to alleviate the gap significantly, across 46 robustness checks. Our diagnosis, therefore, yields a simple and deployable fix, a reflective-threshold gate that withholds amplification until a tweet is predicted to draw thoughtful engagement, which we find to reallocate exposure away from low-credibility content at no cost to mainstream exposure and with no loss of engagement.
