# Toxic comments reduce the activity of volunteer editors on Wikipedia

arXiv: https://arxiv.org/abs/2304.13568 · HTML: https://mlsanity.com/paper/2304.13568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 75 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [A New Generation of Perspective API: Efficient Multilingual Character-level Transformers](https://mlsanity.com/paper/2202.11176/) (2022)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Analyzing the Targets of Hate in Online Social Media](https://mlsanity.com/paper/1603.07709/) (2016)
- [Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective](https://mlsanity.com/paper/2012.12305/) (2020)
- [It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia](https://mlsanity.com/paper/1501.06307/) (2015)
- [Controlled Analyses of Social Biases in Wikipedia Bios](https://mlsanity.com/paper/2101.00078/) (2020)
- [Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying](https://mlsanity.com/paper/1702.07784/) (2017)
- [Why We Read Wikipedia](https://mlsanity.com/paper/1702.05379/) (2017)
- [Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community](https://mlsanity.com/paper/2109.07346/) (2021)
- [Measuring the Importance of User-Generated Content to Search Engines](https://mlsanity.com/paper/1906.08576/) (2019)
- [How Community Feedback Shapes User Behavior](https://mlsanity.com/paper/1405.1429/) (2014)
- [Volunteer contributions to Wikipedia increased during COVID-19 mobility restrictions](https://mlsanity.com/paper/2102.10090/) (2021)

## Abstract (arXiv)

Wikipedia is one of the most successful collaborative projects in history. It is the largest encyclopedia ever created, with millions of users worldwide relying on it as the first source of information as well as for fact-checking and in-depth research. As Wikipedia relies solely on the efforts of its volunteer-editors, its success might be particularly affected by toxic speech. In this paper, we analyze all 57 million comments made on user talk pages of 8.5 million editors across the six most active language editions of Wikipedia to study the potential impact of toxicity on editors' behaviour. We find that toxic comments consistently reduce the activity of editors, leading to an estimated loss of 0.5-2 active days per user in the short term. This amounts to multiple human-years of lost productivity when considering the number of active contributors to Wikipedia. The effects of toxic comments are even greater in the long term, as they significantly increase the risk of editors leaving the project altogether. Using an agent-based model, we demonstrate that toxicity attacks on Wikipedia have the potential to impede the progress of the entire project. Our results underscore the importance of mitigating toxic speech on collaborative platforms such as Wikipedia to ensure their continued success.