Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster
This addresses the bottleneck of high-volume content moderation for social media platforms, though it is incremental as it builds on existing hate speech detection research.
The study tackled the problem of content moderation speed by investigating the effect of model explanations on real-world moderators, finding that structured explanations reduced decision-making time by 7.4%.
Content moderators play a key role in keeping the conversation on social media healthy. While the high volume of content they need to judge represents a bottleneck to the moderation pipeline, no studies have explored how models could support them to make faster decisions. There is, by now, a vast body of research into detecting hate speech, sometimes explicitly motivated by a desire to help improve content moderation, but published research using real content moderators is scarce. In this work we investigate the effect of explanations on the speed of real-world moderators. Our experiments show that while generic explanations do not affect their speed and are often ignored, structured explanations lower moderators' decision making time by 7.4%.