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A two-stage classification model is built in the research for online sexual predator identification. The first stage identifies the suspicious conversations that have predator participants. The second stage identifies the predators in suspicious conversations. Support vector machines are used with word and character n-grams, combined with behavioural features of the authors to train the final classifier. The unbalanced dataset is downsampled to test the performance of re-balancing an unbalanced dataset. An age group classification model is also constructed to test the feasibility of extracting the age profile of the authors, which can be used as features for classifier training. The e↵ect of re-balancing the unbalanced dataset resulted in a better performance of the classifier. Testing the two-stage classification model on the unseen test set, 171 out of 254 predators are successfully identified giving a precision of 0.85, recall of 0.67 and f-score of 0.807. Comparing the classification performance with and without the behavioural feature, it can be seen the n-gram contributed the most to the performance of the classifier, while the behavioural features do not contribute significantly to the performance.
Li, Y. (2020). Identifying online sexual predators using support vector machine. Masters Dissertation. Technological University Dublin. DOI:10.21427/20ba-8g14