论文标题
#TULSAFLOP：算法对Tiktok的集体行动的案例研究
#TulsaFlop: A Case Study of Algorithmically-Influenced Collective Action on TikTok
论文作者
论文摘要
当美国总统的连任集会吸引了比俄克拉荷马州塔尔萨预期的人群小的人群时，许多人将低投票率归因于Tiktok用户组织的集体行动。这项工作是出于Tiktok的流行及其日益增长的社会政治意义的激励，探讨了Tiktok推荐算法在扩大呼吁行动视频中的作用，该视频促进了针对塔尔萨集会的集体行动。我们分析了来自600多个Tiktok用户的召唤视频，并将这些视频的可见性（即播放计数）与同一用户发布的其他视频进行比较。有证据表明，与塔尔萨相关的视频通常收到更多的戏剧，在某些情况下，放大是戏剧性的。例如，播放了一个用户的召唤视频超过200万次，但用户没有其他视频超过100,000播放，并且用户的关注者少于20,000。统计模型表明，增加的游戏计数是通过增加参与度而不是任何系统的召唤视频放大来解释的。我们通过讨论推荐算法扩大社会政治信息的含义，并激发了未来工作的几个有希望的领域。
When a re-election rally for the U.S. president drew smaller crowds than expected in Tulsa, Oklahoma, many people attributed the low turnout to collective action organized by TikTok users. Motivated by TikTok's surge in popularity and its growing sociopolitical implications, this work explores the role of TikTok's recommender algorithm in amplifying call-to-action videos that promoted collective action against the Tulsa rally. We analyze call-to-action videos from more than 600 TikTok users and compare the visibility (i.e. play count) of these videos with other videos published by the same users. Evidence suggests that Tulsa-related videos generally received more plays, and in some cases the amplification was dramatic. For example, one user's call-to-action video was played over 2 million times, but no other video by the user exceeded 100,000 plays, and the user had fewer than 20,000 followers. Statistical modeling suggests that the increased play count is explained by increased engagement rather than any systematic amplification of call-to-action videos. We conclude by discussing the implications of recommender algorithms amplifying sociopolitical messages, and motivate several promising areas for future work.
