An Intelligent Online Grooming Detection System Using AI Technologies

Anderson, Philip, Zou, Zheming, Yang, Longzhi and Qu, Yanpeng (2019) An Intelligent Online Grooming Detection System Using AI Technologies. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, Piscataway, NJ. ISBN 9781538617281

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Abstract

The rapid expansion of the Internet has experienced a significant increase in cases of child abuse, as more and more young children have greater access to the Internet. In particular, adults and minors are able to exchange sexually explicit messages and media via a variety of online platforms that are widely available, which leads to an increasing concern of child grooming. Traditionally, the identification of child grooming relies on the analysis and localisation of conversation texts, but this is usually time-consuming and associated with other implications such as psychological pressure on the investigators. Therefore, automatic methods to detect grooming conversations have attracted the attention of many researchers. This paper proposes such a system to identify child grooming in online chat conversations, where the training data of the system were harvested from publicly available information. The data processing is based on a group of AI technologies, including fuzzy-rough feature selection and fuzzy twin support vector machine. Evaluation shows the promise of the proposed approach in identifying online grooming conversations to be implemented in the future after further development to support real-world cases.

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