Early detection of deception and aggressiveness using profile-based representations

E-communication represents a major threat to users who are exposed to a number of risks and potential attacks. Detecting these risks with as much anticipation as possible is crucial for prevention. However, much research so far has focused on forensic tools that can be applied only when an attack has been performed. This paper proposes a novel and effective methodology for the early detection of threats in written social media. The goal is to recognize a potential attack before it is consummated, and using a minimum amount of information. The proposed approach considers the use of profile-based representations (PBRs) for this goal. PBRs have multiple benefits, including non-sparsity, low dimensionality, and a proved discriminative power. Moreover, representations for partial documents can be derived naturally with PBRs, which makes them suitable for the addressed problem. Results include empirical evidence on the usefulness of PBRs in the early recognition setting for two tasks in which anticipation is critical: sexual predator detection and aggressive text identification. These results reveal, on the one hand, that PBRs achieve state of the art performance when using full-length documents (i.e., the classical task), and, on the other hand, that the proposed methodology outperforms previous work on early recognition of sexual predators by a considerable margin, while obtaining state of the art performance in aggressive text identification. To the best of our knowledge, these are the best results reported on early recognition for the approached problems. We foresee this work will pave the way for the development of novel methodologies for the problem and will motivate further research from the intelligent systems and text mining communities.