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Automatic analysis and identification of verbal aggression and abusive behaviors for online social games
Affiliation:1. Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia;2. Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia;3. Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, 884000 Kota Kinabalu, Sabah, Malaysia;1. Department of Chemistry, Sciences & Arts Faculty, Sakarya University, 54187 Sakarya, Turkey;2. Department of Metallurgy and Material Engineering, Engineering Faculty, Sakarya University, 54187 Sakarya, Turkey
Abstract:Online multiplayer games create new social platforms, with their own etiquette, social rules of conduct and ways of expression. What counts as aggressive and abusing behavior may change depending on the platform, but most online gaming companies need to deal with aggressive and abusive players explicitly. This usually is tied to a reporting mechanism where the offended player reports an offense. In this paper, we develop tools for validating whether a verbal aggression offense report refers to a real offense or not, in the context of a very popular online social game, called Okey. Our approach relies on the analysis of player behavior and characteristics of offending players. In the proposed system, chat records and other social activities in the game are taken into account, as well as player history. This methodology is sufficiently generic, and it can be applied to similar gaming platforms, thus describing a useful tool for game companies. We report our results on data collected over a six months period, involving 100,000 users and 800,000 game records, and illustrate the viability of such analysis, while providing insights on the factors associated with verbal aggression and abusive behavior for social games.
Keywords:Online social games  Sociability  Verbal aggression  Abusive behavior  Chat analysis  Cyberbullying  Machine learning
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