# Machine learning and semantic analysis of in-game chat for cyberbullying

arXiv: https://arxiv.org/abs/1907.10855 · HTML: https://mlsanity.com/paper/1907.10855/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

## Built on by (most cited first, 7 of 7)

- [Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection](https://mlsanity.com/paper/2106.04681/) (2021)
- [CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection](https://mlsanity.com/paper/2106.06213/) (2021)
- [AI in (and for) Games](https://mlsanity.com/paper/2105.03123/) (2021)
- [Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying](https://mlsanity.com/paper/2404.03686/) (2024)
- ["bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games](https://mlsanity.com/paper/2604.10175/) (2026)
- [Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities](https://mlsanity.com/paper/2607.07277/) (2026)
- [LURE: a Phase-Labelled Synthetic Corpus of Online-Grooming Exchanges](https://mlsanity.com/paper/2610.15908/) (2026)

## Abstract (arXiv)

One major problem with cyberbullying research is the lack of data, since researchers are traditionally forced to rely on survey data where victims and perpetrators self-report their impressions. In this paper, an automatic data collection system is presented that continuously collects in-game chat data from one of the most popular online multi-player games: World of Tanks. The data was collected and combined with other information about the players from available online data services. It presents a scoring scheme to enable identification of cyberbullying based on current research. Classification of the collected data was carried out using simple feature detection with SQL database queries and compared to classification from AI-based sentiment text analysis services that have recently become available and further against manually classified data using a custom-built classification client built for this paper. The simple SQL classification proved to be quite useful at identifying some features of toxic chat such as the use of bad language or racist sentiments, however the classification by the more sophisticated online sentiment analysis services proved to be disappointing. The results were then examined for insights into cyberbullying within this game and it was shown that it should be possible to reduce cyberbullying within the World of Tanks game by a significant factor by simply freezing the player's ability to communicate through the in-game chat function for a short period after the player is killed within a match. It was also shown that very new players are much less likely to engage in cyberbullying, suggesting that it may be a learned behaviour from other players.