Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

22 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article numberpgac093
Number of pages15
Journal / PublicationPNAS Nexus
Volume1
Issue number3
Online published5 Jul 2022
Publication statusPublished - Jul 2022

Link(s)

Abstract

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multi-national data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar was found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-negligible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

Research Area(s)

  • COVID-19, social distancing, hygiene, policy support, psychology, machine learning, public health measures

Citation Format(s)

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. / 236 authors, including; Pavlović, Tomislav; Huang, Guanxiong et al.
In: PNAS Nexus, Vol. 1, No. 3, pgac093, 07.2022.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Download Statistics

No data available