# Norm violation in online communities -- A study of Stack Overflow comments

arXiv: https://arxiv.org/abs/2004.05589 · HTML: https://mlsanity.com/paper/2004.05589/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Analyzing Norm Violations in Live-Stream Chat](https://mlsanity.com/paper/2305.10731/) (2023)
- [Learning for Detecting Norm Violation in Online Communities](https://mlsanity.com/paper/2104.14911/) (2021)
- [ChatGPT (Feb 13 Version) is a Chinese Room](https://mlsanity.com/paper/2304.12411/) (2023)
- [Social Value Orientation and Integral Emotions in Multi-Agent Systems](https://mlsanity.com/paper/2305.05549/) (2023)
- [Ultra Low-Cost Two-Stage Multimodal System for Non-Normative Behavior Detection](https://mlsanity.com/paper/2403.16151/) (2024)

## Abstract (arXiv)

Norms are behavioral expectations in communities. Online communities are also expected to abide by the rules and regulations that are expressed in the code of conduct of a system. Even though community authorities continuously prompt their users to follow the regulations, it is observed that hate speech and abusive language usage are on the rise. In this paper, we quantify and analyze the patterns of violations of normative behaviour among the users of Stack Overflow (SO) - a well-known technical question-answer site for professionals and enthusiast programmers, while posting a comment. Even though the site has been dedicated to technical problem solving and debugging, hate speech as well as posting offensive comments make the community "toxic". By identifying and minimising various patterns of norm violations in different SO communities, the community would become less toxic and thereby the community can engage more effectively in its goal of knowledge sharing. Moreover, through automatic detection of such comments, the authors can be warned by the moderators, so that it is less likely to be repeated, thereby the reputation of the site and community can be improved. Based on the comments extracted from two different data sources on SO, this work first presents a taxonomy of norms that are violated. Second, it demonstrates the sanctions for certain norm violations. Third, it proposes a recommendation system that can be used to warn users that they are about to violate a norm. This can help achieve norm adherence in online communities.