Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.04106 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909218462760960 |
|---|---|
| author | Calabrese, Agostina Neves, Leonardo Shah, Neil Bos, Maarten W. Ross, Björn Lapata, Mirella Barbieri, Francesco |
| author_facet | Calabrese, Agostina Neves, Leonardo Shah, Neil Bos, Maarten W. Ross, Björn Lapata, Mirella Barbieri, Francesco |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04106 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster Calabrese, Agostina Neves, Leonardo Shah, Neil Bos, Maarten W. Ross, Björn Lapata, Mirella Barbieri, Francesco Computation and Language 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%. |
| title | Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.04106 |