Machine learning and semantic analysis of in-game chat for cyberbullying
Shane Murnion, William J. Buchanan, Adrian Smales, Gordon Russell

TL;DR
This study develops an automated system to collect and analyze in-game chat data from World of Tanks, identifying cyberbullying patterns and proposing mitigation strategies, with findings on the effectiveness of simple classification methods and player behavior insights.
Contribution
Introduces a novel automated data collection and classification framework for cyberbullying detection in online gaming, comparing SQL-based and AI sentiment analysis methods.
Findings
SQL classification effectively detects toxic language
AI sentiment analysis underperforms in this context
Player behavior analysis suggests new players are less likely to cyberbully
Abstract
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…
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