Predatory Conversation Detection
Parisa Rezaee Borj, Patrick Bours · 2019
Providing a safe environment for children in online networks can be challenged by the anonymous nature of the internet. One of the worst forms of cyber-security issues is child grooming, where sexual predators seek contact with minors to abuse them. Various types of organizations such as chat providers and law enforcement are inclined to find online predatory conversations in order to protect the children. This paper proposes a study on predatory conversation detection using Natural Language Processing. We analyzed the different types of features of online grooming data considering various characteristics of online conversation, such as psycho-linguistic patterns. Our experiments with online communication showed an accuracy of 0.98 in automatically classifying the conversations into predatory conversations and non-predatory conversations. Best results are obtained by using linear SVM on 1-gram features when removing stop words as well as by using multinomial Naïve Bayes on 1-gram features when not removing stop words.