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%.
@article{arxiv.2406.04106,
title = {Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster},
author = {Agostina Calabrese and Leonardo Neves and Neil Shah and Maarten W. Bos and Björn Ross and Mirella Lapata and Francesco Barbieri},
journal= {arXiv preprint arXiv:2406.04106},
year = {2024}
}