Identity Deception Prevention using Common Contribution Network Data

Tsikerdekis, M. (2017).
Identity Deception Prevention using Common Contribution Network Data
IEEE Transactions of Information Forensics and Security. 12(1), 188-199. doi: 10.1109/TIFS.2016.2607697
Impact Factor: 2.441

Abstract
Identity deception in social media applications has negatively impacted online communities and it is likely to increase as the social media user population grows. The ease of generating new accounts on social media has exacerbated the issue. Many previous studies have been posited that focused on both verbal, non-verbal and network data produced by users in an attempt to detect identity deception. However, although these methods produced a high accuracy, they are mainly reactive to the issue of identity deception. This paper proposes a proactive approach that leverages social network data and it is focused on identity deception prevention for online sub-communities, communities that exist within larger communities (e.g., Facebook groups or Subreddits). The method can be applied to various types of social media applications and produces high accuracy in identifying deceptive accounts at the time of attempted entry to a sub-community. Performance results as well as limitations for the method are presented. A discussion follows on the identification of possible implications of this study for social media applications and future directions on deception prevention are proposed.

Download