# Automated Content Moderation Increases Adherence to Community Guidelines

arXiv: https://arxiv.org/abs/2210.10454 · HTML: https://mlsanity.com/paper/2210.10454/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2022.

## Built on by (most cited first, 10 of 10)

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- [Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement](https://mlsanity.com/paper/2507.07640/) (2025)
- [Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation](https://mlsanity.com/paper/2410.23448/) (2024)
- [Community Moderation and the New Epistemology of Fact Checking on Social Media](https://mlsanity.com/paper/2505.20067/) (2025)
- [Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Comments](https://mlsanity.com/paper/2505.18927/) (2025)
- [Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions](https://mlsanity.com/paper/2310.05779/) (2023)
- [Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation](https://mlsanity.com/paper/2503.16505/) (2025)
- [Wikipedia is Not a Dictionary, Delete! Text Classification as a Proxy for Analysing Wiki Deletion Discussions](https://mlsanity.com/paper/2503.10294/) (2025)
- [Context-Aware Content Moderation for German Newspaper Comments](https://mlsanity.com/paper/2505.20963/) (2025)
- [Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch](https://mlsanity.com/paper/2506.07667/) (2025)

## 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.