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

Type of content
Journal Article
Thesis discipline
Degree name
Publisher
Oxford University Press (OUP)
Journal Title
Journal ISSN
Volume Title
Language
en
Date
2022
Authors
Pavlović T
Azevedo F
De K
Riaño-Moreno JC
Maglić M
Gkinopoulos T
Donnelly-Kehoe PA
Payán-Gómez C
Huang G
Kantorowicz J
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.

Description
Citation
Pavlović T, Azevedo F, De K, Riaño-Moreno JC, Maglić M, Gkinopoulos T, Donnelly-Kehoe PA, Payán-Gómez C, Huang G, Kantorowicz J, Birtel MD, Schönegger P, Capraro V, Santamaría-García H, Yucel M, Ibanez A, Rathje S, Wetter E, Stanojević D, van Prooijen J-W, Hesse E, Elbaek CT, Franc R, Pavlović Z, Mitkidis P, Cichocka A, Gelfand M, Alfano M, Ross RM, Sjåstad H, Nezlek JB, Cislak A, Lockwood P, Abts K, Agadullina E, Amodio DM, Apps MAJ, Aruta JJB, Besharati S, Bor A, Choma B, Cunningham W, Ejaz W, Farmer H, Findor A, Gjoneska B, Gualda E, Huynh TLD, Imran MA, Israelashvili J, Kantorowicz-Reznichenko E, Krouwel A, Kutiyski Y, Laakasuo M, Lamm C, Levy J, Leygue C, Lin M-J, Mansoor MS, Marie A, Mayiwar L, Mazepus H, McHugh C, Olsson A, Otterbring T, Packer D, Palomäki J, Perry A, Petersen MB, Puthillam A, Rothmund T, Schmid PC, Stadelmann D, Stoica A, Stoyanov D, Stoyanova K, Tewari S, Todosijević B, Torgler B, Tsakiris M, Tung HH, Umbreș RG, Vanags E, Vlasceanu M, Vonasch AJ, Zhang Y, Abad M, Adler E, Mdarhri HA, Antazo B, Ay FC, Ba MEH, Barbosa S, Bastian B, Berg A, Białek M, Bilancini E, Bogatyreva N, Boncinelli L, Booth JE, Borau S, Buchel O, de Carvalho CF, Celadin T, Cerami C, Chalise HN, Cheng X, Cian L, Cockcroft K, Conway J, Córdoba-Delgado MA, Crespi C, Crouzevialle M, Cutler J, Cypryańska M, Dabrowska J, Davis VH, Minda JP, Dayley PN, Delouvée S, Denkovski O, Dezecache G, Dhaliwal NA, Diato A, Paolo RD, Dulleck U, Ekmanis J, Etienne TW, Farhana HH, Farkhari F, Fidanovski K, Flew T, Fraser S, Frempong RB, Fugelsang J, Gale J, García-Navarro EB, Garladinne P, Gray K, Griffin SM, Gronfeldt B, Gruber J, Halperin E, Herzon V, Hruška M, Hudecek MFC, Isler O, Jangard S, Jørgensen F, Keudel O, Koppel L, Koverola M, Kunnari A, Leota J, Lermer E, Li C, Longoni C, McCashin D, Mikloušić I, Molina-Paredes J, Monroy-Fonseca C, Morales-Marente E, Moreau D, Muda R, Myer A, Nash K, Nitschke JP, Nurse MS, de Mello VO, Palacios-Galvez MS, Palomäki J, Pan Y, Papp Z, Pärnamets P, Paruzel-Czachura M, Perander S, Pitman M, Raza A, Rêgo GG, Robertson C, Rodríguez-Pascual I, Saikkonen T, Salvador-Ginez O, Sampaio WM, Santi GC, Schultner D, Schutte E, Scott A, Skali A, Stefaniak A, Sternisko A, Strickland B, Strickland B, Thomas JP, Tinghög G, Traast IJ, Tucciarelli R, Tyrala M, Ungson ND, Uysal MS, Van Rooy D, Västfjäll D, Vieira JB, von Sikorski C, Walker AC, Watermeyer J, Willardt R, Wohl MJA, Wójcik AD, Wu K, Yamada Y, Yilmaz O, Yogeeswaran K, Ziemer C-T, Zwaan RA, Boggio PS, Whillans A, Van Lange PAM, Prasad R, Onderco M, O'Madagain C, Nesh-Nash T, Laguna OM, Kutiyski Y, Kubin E, Gümren M, Fenwick A, Ertan AS, Bernstein MJ, Amara H, Van Bavel JJ Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS Nexus.
Keywords
COVID-19, social distancing, hygiene, policy support, psychology, machine learning, public health measures
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
Fields of Research::52 - Psychology
Fields of Research::46 - Information and computing sciences::4611 - Machine learning
Fields of Research::42 - Health sciences
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All rights reserved unless otherwise stated