curated PJ Dataset

The risks to children of online predators in real time gaming environments have been an area of growing concern. Research towards the development of near real time capabilities has been the focus of most queries published in this area of study. In this paper, we present Protectbot, a comprehensive safety framework used to interact with users in online gaming chat rooms. Protectbot employs a variant of the GPT-2 model known as DialoGPT, a generative pre-trained transformer designed specifically for conversation. By generating content that closely resembles human dialogue, DialoGPT allows Protectbot to engage users in interactive chat sessions. At the end of each chat, Protectbot analyzes the user's messages to identify any indications of potentially predatory behavior, enhancing the platform's capacity to safeguard its users. Protectbot architecture implements a text classifier that was trained and tested on the PAN12 dataset for identifying sexual predators. fastText word embeddings are generated from the chat text and aggregated into sentence vectors, which are then used as input features to train an SVM classifier. The proposed model achieved notable performance metrics, with a recall, accuracy, F1-score, and F_0.5-score of 0.99, marking a significant improvement over previous methodologies. A new dataset is prepared based on 71 predatory chats obtained from Perverted Justice (PJ), to evaluate the classifier's performance. The proposed approach demonstrates a high true positive rate of classifying predatory behavior by replacing the SVM with the KNN classifier