Title: Stylometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis

Abstract:  Vendors’ trustworthiness on darknet markets is associated with an anonymous identity. Both buyers and vendors, especially influential ones, tend to migrate to new markets when a previously used market shuts down.  A better understanding of the signaling strategies used by darknet market vendors for establishing trustworthiness in their products requires linking users’ identities as they migrate across darknet forums. We develop a stylometry-based multitask learning approach for natural language and interaction modeling using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution. We provide a comprehensive evaluation of our methods across four different darknet forums demonstrating its efficacy over the state-of-the-art, with a lift of up to 2.5x on Mean Retrieval Rank and 2x on Recall@10.