Enabling Contextual Soft Moderation on Social Media through Contrastive Textual Deviation
This addresses the issue of inaccurate moderation that undermines trust in health experts and desensitizes users, though it is incremental as it builds on existing pipelines.
The paper tackled the problem of contextual false positives in automated soft moderation systems on social media by incorporating stance detection, reducing false positives from 20% to 2.1%.
Automated soft moderation systems are unable to ascertain if a post supports or refutes a false claim, resulting in a large number of contextual false positives. This limits their effectiveness, for example undermining trust in health experts by adding warnings to their posts or resorting to vague warnings instead of granular fact-checks, which result in desensitizing users. In this paper, we propose to incorporate stance detection into existing automated soft-moderation pipelines, with the goal of ruling out contextual false positives and providing more precise recommendations for social media content that should receive warnings. We develop a textual deviation task called Contrastive Textual Deviation (CTD) and show that it outperforms existing stance detection approaches when applied to soft moderation.We then integrate CTD into the stateof-the-art system for automated soft moderation Lambretta, showing that our approach can reduce contextual false positives from 20% to 2.1%, providing another important building block towards deploying reliable automated soft moderation tools on social media.