# Antisocial Behavior in Online Discussion Communities

arXiv: https://arxiv.org/abs/1504.00680 · HTML: https://mlsanity.com/paper/1504.00680/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2015.

## Built on by (most cited first, 16 of 16)

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- [Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling](https://mlsanity.com/paper/2410.04164/) (2024)
- [Conversation Modeling to Predict Derailment](https://mlsanity.com/paper/2303.11184/) (2023)
- [Towards Non-Adversarial Algorithmic Recourse](https://mlsanity.com/paper/2403.10330/) (2024)
- [Taking a Language Detour: How International Migrants Speaking a Minority Language Seek COVID-Related Information in Their Host Countries](https://mlsanity.com/paper/2209.02903/) (2022)
- [Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai](https://mlsanity.com/paper/2601.01090/) (2026)
- [Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior](https://mlsanity.com/paper/2507.20614/) (2025)

## Abstract (arXiv)

User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Further, we discover that antisocial behavior is exacerbated when community feedback is overly harsh. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We use these insights to identify antisocial users early on, a task of high practical importance to community maintainers.