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 lexic al 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 decep- tion 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 pro- posed 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.

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Published in IEEE Transactions on Information Forensics and Security, v. 9, no. 8, p. 1311-1321.

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