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

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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 multinational 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 results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible 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.


Funder: Institute of Social Sciences Ivo Pilar, Zagreb; Croatia


Funder: National Institutes of Health; DOI:

Funder: Ministry of Science and Technology; DOI:

Funder: QUT Centre for Behavioural Economics, Society and Technology

Funder: Sistema Nacional de Investigadores; DOI:


Funder: NOMIS Foundation; DOI:

Funder: University of Huelva; DOI:

Funder: University of Vienna; DOI:

Funder: Natural Sciences and Engineering Research Council of Canada; DOI:

Funder: Universidad del Rosario; DOI:

Funder: Batten Institute; DOI:

Funder: University of Virginia Darden School of Business; DOI:


public health measures, COVID-19, social distancing, policy support, hygiene

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Oxford University Press
VolkswagenStiftung (98 525)
Slovak Research and Development Agency (APVV-17-0596, APVV-18-0218)
Medical Research Council (MR/P014097/1)
Carlsberg Foundation (CF20-044)
NWO (440.20.003)
Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-9/2021-14/200163)
National Natural Science Foundation of China (71972065, 71832004, 71872152)
Universities in Hebei Province Hundred Outstanding Innovative Talents Support Program (SLRC2019002)
Ministry of Education of China (21JHQ088)
French National Research Agency (ANR-10-IDEX-0001-02 PSL*, ANR-10-LABX-0087 IEC, ANR-17-EURE-0017, ANR-17-EURE-0010)
Croatian Science Foundation (DOK-01-2018)
FONDECYT (1210195, 1210176)
FonCyT (1820)
Sistema General de Regalías de Colombia (BPIN2018000100059)
the National Institutes of Aging (R01 AG057234)
Alzheimer's Association (SG-20-725707)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (1133/2019)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (309905/2019-2)
São Paulo Research Foundation (2019/26665-5, 2019/27100-1)
Australian Research Council (DP180102384)
John Templeton Foundation (61378)
National Science Centre (2018/29/B/HS6/02826)
Research Council of Norway (262675)
Social Sciences and Humanities Research Council of Canada (506547, 435-2019-0692)
Aarhus University Research Foundation (28207, AUFF-E-201 9-9-4)
Deutsche Forschungsgemeinschaft (EXC 2052/1-390713894)
Biotechnology and Biological Sciences Research Council (BB/R010668/1)
Jane and Aatos Erkko Foundation (170112)
Academy of Finland (323207)
Austrian Science Fund (FWF, I3381)
Swedish Research Council Formas (2018-00877)