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When Those With Privacy Concerns Show Stronger In-Group Favoritism: Using Personal Information From In-Group and Out-Group Members to Identify Terrorist Threats

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  • Torsten Reimer
  • Nathanael Johnson

Abstract

Terrorist threats and attacks provide major risks and sources of public crises in the 21st century. New probabilistic computing technologies possess the capability of increasing the success of identifying terrorist threats and solving cybersecurity and encryption problems more efficiently. However, to identify terrorist threats, these technologies would require the use of a large amount of personal data, which cause potential concerns for privacy. We offer a social-identity explanation of public support for the detection of terrorist threats through the use of online personal data. A survey study ( N  = 1,204) revealed strong support for the provided social-identity explanation of public perceptions. As expected, respondents displayed in-group favoritism by more strongly supporting the use of private personal information from out-group members (non-U.S. citizens) than from in-group members (U.S. citizens). The observed in-group favoritism was most pronounced when respondents had both a strong national identity and a strong sense of general privacy concern. The observed differences were independent of respondents’ political orientation and age.

Suggested Citation

  • Torsten Reimer & Nathanael Johnson, 2023. "When Those With Privacy Concerns Show Stronger In-Group Favoritism: Using Personal Information From In-Group and Out-Group Members to Identify Terrorist Threats," SAGE Open, , vol. 13(3), pages 21582440231, July.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:3:p:21582440231184422
    DOI: 10.1177/21582440231184422
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    References listed on IDEAS

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    1. Leonie Huddy & Nadia Khatib, 2007. "American Patriotism, National Identity, and Political Involvement," American Journal of Political Science, John Wiley & Sons, vol. 51(1), pages 63-77, January.
    2. Katya Drozdova & Michael Samoilov, 2010. "Predictive analysis of concealed social network activities based on communication technology choices: early-warning detection of attack signals from terrorist organizations," Computational and Mathematical Organization Theory, Springer, vol. 16(1), pages 61-88, March.
    3. Berinsky, Adam J. & Huber, Gregory A. & Lenz, Gabriel S., 2012. "Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk," Political Analysis, Cambridge University Press, vol. 20(3), pages 351-368, July.
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