Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster
Agostina Calabrese, Leonardo Neves, Neil Shah, Maarten Bos, Björn Ross, Mirella Lapata, Francesco Barbieri
Abstract
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%.
BibTeX
@inproceedings{calabrese-etal-2024-explainability,
title = "Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster",
author = {Calabrese, Agostina and
Neves, Leonardo and
Shah, Neil and
Bos, Maarten and
Ross, Bj{\"o}rn and
Lapata, Mirella and
Barbieri, Francesco},
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-short.38/",
doi = "10.18653/v1/2024.acl-short.38",
pages = "398--408"
}