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Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

Author

Listed:
  • Max Ismailov

    (Computer Science Department, Western Washington University, Bellingham, WA 98225, USA)

  • Michail Tsikerdekis

    (Computer Science Department, Western Washington University, Bellingham, WA 98225, USA)

  • Sherali Zeadally

    (College of Communication and Information, University of Kentucky, Lexington, KY 40506, USA)

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.

Suggested Citation

  • Max Ismailov & Michail Tsikerdekis & Sherali Zeadally, 2020. "Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation," Future Internet, MDPI, vol. 12(9), pages 1-12, August.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:9:p:148-:d:406853
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    References listed on IDEAS

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    1. Alessandro Lovari, 2020. "Spreading (Dis)Trust: Covid-19 Misinformation and Government Intervention in Italy," Media and Communication, Cogitatio Press, vol. 8(2), pages 458-461.
    2. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    3. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
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    Cited by:

    1. José Manuel Ortiz-Marcos & María Tomé-Fernández & Christian Fernández-Leyva, 2021. "Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions," Future Internet, MDPI, vol. 13(1), pages 1-15, January.

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    Keywords

    identity; deception; detection;
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