  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.
