Automated Content Moderation Increases Adherence to Community Guidelines
Not on the map. Why?
Abstract (arXiv)
Online social media platforms use automated moderation systems to remove or reduce the visibility of rule-breaking content. While previous work has documented the importance of manual content moderation, the effects of automated content moderation remain largely unknown. Here, in a large study of Facebook comments (n=412M), we used a fuzzy regression discontinuity design to measure the impact of automated content moderation on subsequent rule-breaking behavior (number of comments hidden/deleted) and engagement (number of additional comments posted). We found that comment deletion decreased subsequent rule-breaking behavior in shorter threads (20 or fewer comments), even among other participants, suggesting that the intervention prevented conversations from derailing. Further, the effect of deletion on the affected user's subsequent rule-breaking behavior was longer-lived than its effect on reducing commenting in general, suggesting that users were deterred from rule-breaking but not from commenting. In contrast, hiding (rather than deleting) content had small and statistically insignificant effects. Our results suggest that automated content moderation increases adherence to community guidelines.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Oct 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 8.
10 papers.
- 2025The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrievalcited by 14
- 2025Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacementcited by 10
- 2024Venire: A Machine Learning-Guided Panel Review System for Community Content Moderationcited by 7
- 2025Community Moderation and the New Epistemology of Fact Checking on Social Mediacited by 5
- 2025Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Commentscited by 3
- 2023Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussionscited by 2
- 2025Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitationcited by 2
- 2025Wikipedia is Not a Dictionary, Delete! Text Classification as a Proxy for Analysing Wiki Deletion Discussionscited by 0
- 2025Context-Aware Content Moderation for German Newspaper Commentscited by 0
- 2025Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitchcited by 0
- 2025Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement10 Jul 2025
- 2025Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch9 Jun 2025
- 2025Context-Aware Content Moderation for German Newspaper Comments27 May 2025
- 2025Community Moderation and the New Epistemology of Fact Checking on Social Media26 May 2025
- 2025Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Comments25 May 2025
- 2025The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval19 Mar 2025
- 2025Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation13 Mar 2025
- 2025Wikipedia is Not a Dictionary, Delete! Text Classification as a Proxy for Analysing Wiki Deletion Discussions13 Mar 2025
- 2024Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation30 Oct 2024
- 2023Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions9 Oct 2023