Abstract

Content-based authorship identification is an emerging research problem in online social media networks, due to a wide collection of issues ranging from security to privacy preservation, from radicalization to defamation detection, and so forth. Indeed, this research has attracted a relevant amount of attention from the research community during the past years. The general problem becomes harder when we consider the additional constraint of identifying the same false profile over different social media networks, under obvious considerations. Inspired by this emerging research challenge, in this paper we propose and experimentally assess an innovative framework for supporting content-based authorship identification and analysis in social media networks.