Abstract
Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments.
Document Type
Review
Publication Date
8-31-2020
Digital Object Identifier (DOI)
https://doi.org/10.3390/fi12090148
Repository Citation
Ismailov, Max; Tsikerdekis, Michail; and Zeadally, Sherali, "Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation" (2020). Information Science Faculty Publications. 73.
https://uknowledge.uky.edu/slis_facpub/73
Notes/Citation Information
Published in Future Internet, v. 12, issue 9, 148.
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).