Detecting Sexual Predatory Chats by Perturbed Data and Balanced Ensembles Effects

Abstract

Securing the safety of the children on online platforms is critical to avoid the mishaps of them being abused for sexual favors, which usually happens through predatory conversations. A number of approaches have been proposed to analyze the content of the messages to identify predatory conversations. However, due to the non-availability of large-scale predatory data, the stateof-the-art works employ a standard dataset that has less than 10% predatory conversations. Dealing with such heavy class imbalance is a challenge to devise reliable predatory detection approaches. We present a new approach for dealing with class imbalance using a hybrid sampling and class re-distribution to obtain an augmented dataset. To further improve the diversity of classifiers and features in the ensembles, we also propose to perturb the data along with augmentation in an iterative manner. Through a set of experiments, we demonstrate an improvement of 3% over the best stateof-the-art approach and results in an F1-score of 0.99 and an Fβ of 0.94 from the proposed approach.