Multiple account identity deception detection in social media using nonverbal behavior

Michail Tsikerdekis, Sherali Zeadally

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

Abstract

Identity deception has become an increasingly important issue in the social media environment. The case of blocked users initiating new accounts, often called sockpuppetry, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal behavior (e.g., using profile data or lexical features in text). Although these methods yield a high detection accuracy rate, they are computationally inefficient for the social media environment, which often involves databases with large volumes of data. To date, little attention has been paid to detecting online deception using nonverbal behavior. We present a detection method based on nonverbal behavior for identity deception, which can be applied to many types of social media. Using Wikipedia as an experimental case, we demonstrate that our proposed method results in high detection accuracy over previous methods proposed while being computationally efficient for the social media environment. We also demonstrate the potential of nonverbal behavior data that exists in social media and how designers and developers can leverage such nonverbal information in detecting deception to safeguard their online communities.

Original languageEnglish
Article number6843931
Pages (from-to)1311-1321
Number of pages11
JournalIEEE Transactions on Information Forensics and Security
Volume9
Issue number8
DOIs
StatePublished - Aug 2014

Keywords

  • Algorithm
  • deception
  • identity
  • performance
  • social media.

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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