Detecting Predatory Behavior in Game Chats

被引:17
作者
Cheong, Yun-Gyung [1 ]
Jensen, Alaina K.
Gudnadottir, Elin Rut [2 ]
Bae, Byung-Chull [3 ]
Togelius, Julian [4 ]
机构
[1] Sungkyunkwan Univ, Dept Comp Engn, Suwon 440746, South Korea
[2] Financial Supervisory Author, IT Dept, IS-105 Reykjavik, Iceland
[3] Hongik Univ, Sch Games, Sejong 339701, South Korea
[4] NYU, Dept Comp Sci Engn, New York, NY 10003 USA
关键词
Chat; data mining; game data; natural language processing (NLP); preprocessing; sexual predator; text classification;
D O I
10.1109/TCIAIG.2015.2424932
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
While games are a popular social media for children, there is a real risk that these children are exposed to potential sexual assault. A number of studies have already addressed this issue, however, the data used in previous research did not properly represent the real chats found in multiplayer online games. To address this issue, we obtained real chat data from MovieStar-Planet, a massively multiplayer online game for children. The research described in this paper aimed to detect predatory behaviors in the chats using machine learning methods. In order to achieve a high accuracy on this task, extensive preprocessing was necessary. We describe three different strategies for data selection and preprocessing, and extensively compare the performance of different learning algorithms on the different data sets and features.
引用
收藏
页码:220 / 232
页数:13
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