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Detection of Malicious Users with High Influence through Foul Language Network Analysis in MOBA Games
http://doi.org/10.5626/JOK.2018.45.12.1312
In relation to the online game industry, verbal violence in the game has become a serious social problem. However, it is difficult to solve fundamental problems by simply filtering or using reporting systems. This study proposed a method to analyze the propagation tendency of the foul language and to detect malicious users in social network perspective. This method was applied to the analysis of the chat log of Defense of the Ancients 2(DotA 2), a popular MOBA(Multiplayer Online Battle Arena) genre game around the world. In the case of MOBA games, there are usually limited users belonging to one queue, which is a good platform for analyzing foul language networks as compared to other games. Verbally abusive malicious users tend to have high centrality when they form a network. Using these features, we analyzed the propagation tendency of the foul language on the network and detected users with high centrality. We also analyzed the effect on the whole network when the user was restricted. With the proposed method, we were able to detect malicious users who used the foul language. For future works, we will classify the spreading types in the foul language network and analyze users for each type.
User Behavior Analysis for Predicting Churn of Loyal Customers in Online Games based on Social Relationships and Degree of Participation
Eunbi Seo, Jiyoung Woo, Huy Kang Kim
http://doi.org/10.5626/JOK.2018.45.11.1124
Game users in MMORPGs engage in a variety of social activities. However, a few users tend to play games alone, and are designated ‘loners’ similar to modern society. We classified game guilds and game users based on similar user behaviors and community characteristics. We propose a model that predicts churn users by measuring the participation of users in each group. Users in each group show similar behavioral patterns, suggesting that we can classify churn users along with ordinary users. We tested this model for NCsoft’s MMORPG, Aion. Using Randomforest, the recall was measured at an average of 75%.
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