Investigating toxicity in 'Overwatch'

Abstract

This study is centered on the investigation of how machine learning models would perform in automatically detecting toxicity, how much their performance would improve and the measuring of how much toxicity is present in the game chosen. The models that performed the best with the data were SVM and Decision Tree and both reached an accuracy of instances classified correctly of 81.9231%. It was found that the acoustic features used in this study (pitch and intensity) helped with the performance of the models trained to automatically detect toxicity. Although the study presents interesting results and shows that the task of detecting toxicity automatically in general is a feasible one, it is based on a relatively limited sample, such that these results cannot be considered as a representation of the overall population. Lastly, due to the changes in Twitch.tv private policy, streamers were being sanctioned for being “toxic” and some of them got banned from this platform at first, and then started moderating their behavior on their return, which is believed to have affected the efficient gathering of the data.