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Trading Privacy for the Greater Social Good: How Did America React During COVID-19?

Author

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  • Anindya Ghose
  • Beibei Li
  • Meghanath Macha
  • Chenshuo Sun
  • Natasha Ying Zhang Foutz

Abstract

Digital contact tracing and analysis of social distancing from smartphone location data are two prime examples of non-therapeutic interventions used in many countries to mitigate the impact of the COVID-19 pandemic. While many understand the importance of trading personal privacy for the public good, others have been alarmed at the potential for surveillance via measures enabled through location tracking on smartphones. In our research, we analyzed massive yet atomic individual-level location data containing over 22 billion records from ten Blue (Democratic) and ten Red (Republican) cities in the U.S., based on which we present, herein, some of the first evidence of how Americans responded to the increasing concerns that government authorities, the private sector, and public health experts might use individual-level location data to track the COVID-19 spread. First, we found a significant decreasing trend of mobile-app location-sharing opt-out. Whereas areas with more Democrats were more privacy-concerned than areas with more Republicans before the advent of the COVID-19 pandemic, there was a significant decrease in the overall opt-out rates after COVID-19, and this effect was more salient among Democratic than Republican cities. Second, people who practiced social distancing (i.e., those who traveled less and interacted with fewer close contacts during the pandemic) were also less likely to opt-out, whereas the converse was true for people who practiced less social-distancing. This relationship also was more salient among Democratic than Republican cities. Third, high-income populations and males, compared with low-income populations and females, were more privacy-conscientious and more likely to opt-out of location tracking.

Suggested Citation

  • Anindya Ghose & Beibei Li & Meghanath Macha & Chenshuo Sun & Natasha Ying Zhang Foutz, 2020. "Trading Privacy for the Greater Social Good: How Did America React During COVID-19?," Papers 2006.05859, arXiv.org.
  • Handle: RePEc:arx:papers:2006.05859
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    References listed on IDEAS

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    1. Chenshuo Sun & Panagiotis Adamopoulos & Anindya Ghose & Xueming Luo, 2022. "Predicting Stages in Omnichannel Path to Purchase: A Deep Learning Model," Information Systems Research, INFORMS, vol. 33(2), pages 429-445, June.

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