Searching Sexual Predators in Social Network Dialogues

In this paper we propose a two-step technique for detecting sexual predators from social network dialogues. One step for detecting dialogues in which a sexual predators participates, and the second step is for detecting, from the whole dialogue users, the one that is the sexual predator. From the three different supervised classifier employed, Random Forests obtained the best results in the first step, whereas Neural Networks performed best in the second step.