With fact-checking by professionals being difficult to scale on social media, algorithmic techniques have been considered. However, it is uncertain how the public may react to labels by automated fact-checkers. In this study, we investigate the use of automated warning labels derived from misinformation detection literature and investigate their effects on three forms of post engagement. Focusing on political posts, we also consider how partisanship affects engagement. In a two-phases within-subjects experiment with 200 participants, we found that the generic warnings suppressed intents to comment on and share posts, but not on the intent to like them. Furthermore, when different reasons for the labels were provided, their effects on post engagement were inconsistent, suggesting that the reasons could have undesirably motivated engagement instead. Partisanship effects were observed across the labels with higher engagement for politically congruent posts. We discuss the implications on the design and use of automated warning labels.

翻译：由于专业人员的事实核查在社交媒体上难以规模化，算法技术已被纳入考量。然而，公众对自动事实核查员生成的标签有何反应尚不确定。本研究基于错误信息检测领域的文献，探究自动化警告标签的使用及其对三种帖子参与形式的影响。我们聚焦政治类帖子，同时考量党派倾向如何影响参与行为。通过对200名参与者进行两阶段被试内实验，我们发现通用警告抑制了评论和分享帖子的意图，但对点赞意图无显著影响。此外，当提供不同原因标签时，其对帖子参与行为的影响并不一致，这表明原因说明反而可能以一种非预期的方式激发参与行为。党派倾向效应在所有标签中均存在，政治立场一致的帖子获得了更高的参与度。本文讨论了这些发现对自动化警告标签的设计与使用所蕴含的意义。