Authorship Analysis of Online Predatory Conversations using Character Level Convolution Neural Networks

Abstract

Authorship Attribution (AA) of written content presents several advantages within the digital forensics domain. While AA has been traditionally applied to long documents, recent works have shown improved performance of neural AA models on short texts such as tweets and online conversations Concurrently, the rise of social media as well as a plethora of chat messaging platforms have made it easier for teenagers to be vulnerable to online predators. In this work, we present an authorship attribution model that trains on a corpus of online conversations involving predators, and perform subsequent analysis of the message representations. Our results show comparable performance relative to prior work for Authorship Attribution and highlight differences between predatory and non-predatory message styles.