Detecting Predatory Behavior in Game Chats
- Authors
- Cheong, Yun-Gyung; Jensen, Alaina K.; Gudnadottir, Elin Rut; Bae, Byung-Chull; Togelius, Julian
- Issue Date
- Sep-2015
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Chat; data mining; game data; natural language processing (NLP); preprocessing; sexual predator; text classification
- Citation
- IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, v.7, no.3, pp.220 - 232
- Journal Title
- IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES
- Volume
- 7
- Number
- 3
- Start Page
- 220
- End Page
- 232
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/9512
- DOI
- 10.1109/TCIAIG.2015.2424932
- ISSN
- 1943-068X
- Abstract
- 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.
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- Appears in
Collections - School of Games > Game Software Major > 1. Journal Articles
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