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Abstract

As a unique type of social network, the online gaming industry is a fast-growing, changing, and men-dominated field which attracts diverse backgrounds. Being dominated by male users, game developers, game players, game investors, the non-inclusiveness and gender inequality reside as salient problems in the community. In the online gaming communities, most women players report toxic and offensive language or experiences of verbal abuse. Symbolic interactionists and feminists assume that words matter since the use of particular language and terms can dehumanize and harm particular groups such as women. Identifying and reporting the toxic behavior, sexism, and harassment that occur in online games is a critical need in preventing cyberbullying, and it will help gender diversity and equality grow in the online gaming industry. However, the research on this topic is still rare, except for some milestone studies. This paper aims to contribute to the theory and practice of sexist toxic language detection in the online gaming community, through the automatic detection and analysis of toxic comments in online games chat logs. We adopted the MaXQDA tool as a data visualization technique to reveal the most frequently used toxic words used against women in online gaming communities. We also applied the Naïve Bayes Classifier for text mining to classify if a chat log content is sexist and toxic. We also refined the text mining model Laplace estimator and re-tested the model’s accuracy. The study also revealed that the accuracy of the Naïve Bayes Classifier did not change by the Laplace estimator. The findings of the study are expected to raise awareness about the use of gender-based toxic language in the online gaming community. Moreover, the proposed mining model can inspire similar research on practical tools to help moderate the use of sexist toxic language and disinfect these communities from gender-based discrimination and sexist bullying.

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