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Human culture, social interactions, the information ecology, and psychology are crucial elements of the behavioral environment shaping the emergence, transmission, response, and mitigation of infectious diseases. In this chapter, we describe computational theories, models, and methods developed to understand and predict the dynamics of human behavior and the surrounding information environment in the United States of America (U.S.A.) during the COVID-19COVID-19 pandemic. This builds upon and combines research on computational cognitive models, natural language processing, network science, social media, and mass media analysis. One aim is to make predictions about transmission-mitigating behavior, such as mask wearing and social distancing, in relation to regional population variations in demographics, psychographics, politics, weather, social media, news, and perceptions of ongoing pandemic transmission rates. We discuss the specific data pipelines employed, the models, and empirical evaluations. Insights concerning observed behavioral and psychological phenomena are discussed as well as the opportunities for developing population-scale psychological models and information-environment simulators for impactful events.

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Computational Cognitive Theory of Attitudes, Social Information, and Behavior During Pandemics

  • 章节

  • 发表日期：

    2026年

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  • Peter Pirolli,
  • Archna Bhatia,
  • Kathleen M. Carley,
  • Bonnie J. Dorr,
  • Christian Lebiere,
  • Brodie Mather,
  • Konstantinos Mitsopoulos,
  • Don Morrison,
  • Mark Orr,
  • Tomek Strzalkowski,
  • Choh Man Teng

摘要

Human culture, social interactions, the information ecology, and psychology are crucial elements of the behavioral environment shaping the emergence, transmission, response, and mitigation of infectious diseases. In this chapter, we describe computational theories, models, and methods developed to understand and predict the dynamics of human behavior and the surrounding information environment in the United States of America (U.S.A.) during the COVID-19COVID-19 pandemic. This builds upon and combines research on computational cognitive models, natural language processing, network science, social media, and mass media analysis. One aim is to make predictions about transmission-mitigating behavior, such as mask wearing and social distancing, in relation to regional population variations in demographics, psychographics, politics, weather, social media, news, and perceptions of ongoing pandemic transmission rates. We discuss the specific data pipelines employed, the models, and empirical evaluations. Insights concerning observed behavioral and psychological phenomena are discussed as well as the opportunities for developing population-scale psychological models and information-environment simulators for impactful events.