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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.
Evaluation of Structural Changes of a Controlled Group Using Time-Sequential SNA
Woong Lee, Seong-Woong Yoon, Sang-Hoon Lee
A controlled group is closed compared to other organizations, which hinders collection of data and accurate analysis, so that it is hard to evaluate a controlled group’s power structure and predict future changes using usual analytical methods including sociological approach. Analyzing a controlled group using SNA can allow for evaluation of inner power structure by revealing the relationships between members and identifying members with central roles given limited data. In this study, in order to evaluate changes in power structure, time-sequential SNA research was conducted by analyzing eigenvector centrality, which reflects individual influence and reveals the overall power structure. The result showed an improvement in accuracy compared to other centralities that contain individual degree or closeness, and made it possible to presume structural changes such as promotion or purge of a member.
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