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

Pavlović, Tomislav, Azevedo, Flavio, De, Koustav, Riaño-Moreno, Julián C., Maglić, Marina, Gkinopoulos, Theofilos, Donnelly-Kehoe, Patricio Andreas, Payán-Gómez, César, Huang, Guanxiong, Kantorowicz, Jaroslaw, Birtel, Michèle D., Schönegger, Philipp, Capraro, Valerio ORCID logoORCID: https://orcid.org/0000-0002-0579-0166, Santamaría-García, Hernando, Yucel, Meltem, Ibanez, Agustin, Rathje, Steve, Wetter, Erik, Stanojević, Dragan, van Prooijen, Jan-Willem, Hesse, Eugenia, Elbaek, Christian T., Franc, Renata, Pavlović, Zoran, Mitkidis, Panagiotis, Cichocka, Aleksandra, Gelfand, Michele, Alfano, Mark, Ross, Robert M., Sjåstad, Hallgeir, Nezlek, John B., Cislak, Aleksandra, Lockwood, Patricia, Abts, Koen, Agadullina, Elena, Amodio, David M., Apps, Matthew A. J., Aruta, John Jamir Benzon, Besharati, Sahba, Bor, Alexander, Choma, Becky, Cunningham, William, Ejaz, Waqas, Farmer, Harry, Findor, Andrej, Gjoneska, Biljana, Gualda, Estrella, Huynh, Toan L. D., Imran, Mostak Ahamed, Israelashvili, Jacob, Kantorowicz-Reznichenko, Elena, Krouwel, André, Kutiyski, Yordan, Laakasuo, Michael, Lamm, Claus, Levy, Jonathan, Leygue, Caroline, Lin, Ming-Jen, Mansoor, Mohammad Sabbir, Marie, Antoine, Mayiwar, Lewend, Mazepus, Honorata, McHugh, Cillian, Olsson, Andreas, Otterbring, Tobias, Packer, Dominic, Palomäki, Jussi, Perry, Anat, Petersen, Michael Bang, Puthillam, Arathy, Rothmund, Tobias, Schmid, Petra C., Stadelmann, David, Stoica, Augustin, Stoyanov, Drozdstoy, Stoyanova, Kristina, Tewari, Shruti, Todosijević, Bojan, Torgler, Benno, Tsakiris, Manos, Tung, Hans H., Umbreș, Radu Gabriel, Vanags, Edmunds, Vlasceanu, Madalina, Vonasch, Andrew J., Zhang, Yucheng, Abad, Mohcine, Adler, Eli, Mdarhri, Hamza Alaoui, Antazo, Benedict, Ay, F. Ceren, Ba, Mouhamadou El Hady, Barbosa, Sergio, Bastian, Brock, Berg, Anton, Białek, Michał, Bilancini, Ennio, Bogatyreva, Natalia, Boncinelli, Leonardo, Booth, Jonathan E., Borau, Sylvie, Buchel, Ondrej, de Carvalho, Chrissie Ferreira, Celadin, Tatiana, Cerami, Chiara, Chalise, Hom Nath, Cheng, Xiaojun, Cian, Luca, Cockcroft, Kate, Conway, Jane, Córdoba-Delgado, Mateo A., Crespi, Chiara, Crouzevialle, Marie, Cutler, Jo, Cypryańska, Marzena, Dabrowska, Justyna, Davis, Victoria H., Minda, John Paul, Dayley, Pamala N., Delouvée, Sylvain, Denkovski, Ognjan, Dezecache, Guillaume, Dhaliwal, Nathan A., Diato, Alelie, Di Paolo, Roberto, Dulleck, Uwe, Ekmanis, Jānis, Etienne, Tom W., Farhana, Hapsa Hossain, Farkhari, Fahima, Fidanovski, Kristijan, Flew, Terry, Fraser, Shona, Frempong, Raymond Boadi, Fugelsang, Jonathan, Gale, Jessica, García-Navarro, E Begoña, Garladinne, Prasad, Gray, Kurt, Griffin, Siobhán M., Gronfeldt, Bjarki, Gruber, June, Halperin, Eran, Herzon, Volo, Hruška, Matej, Hudecek, Matthias F. C., Isler, Ozan, Jangard, Simon, Jørgensen, Frederik, Keudel, Oleksandra, Koppel, Lina, Koverola, Mika, Kunnari, Anton, Leota, Josh, Lermer, Eva, Li, Chunyun, Longoni, Chiara, McCashin, Darragh, Mikloušić, Igor, Molina-Paredes, Juliana, Monroy-Fonseca, César, Morales-Marente, Elena, Moreau, David, Muda, Rafał, Myer, Annalisa, Nash, Kyle, Nitschke, Jonas P., Nurse, Matthew S., de Mello, Victoria Oldemburgo, Palacios-Galvez, M. Soledad, Pan, Yafeng, Papp, Zsófia, Pärnamets, Philip, Paruzel-Czachura, Mariola, Perander, Silva, Pitman, Michael, Raza, Ali, Rêgo, Gabriel Gaudencio, Robertson, Claire, Rodríguez-Pascual, Iván, Saikkonen, Teemu, Salvador-Ginez, Octavio, Sampaio, Waldir M., Santi, Gaia Chiara, Schultner, David, Schutte, Enid, Scott, Andy, Skali, Ahmed, Stefaniak, Anna, Sternisko, Anni, Strickland, Brent, Thomas, Jeffrey P., Tinghög, Gustav, Traast, Iris J., Tucciarelli, Raffaele, Tyrala, Michael, Ungson, Nick D., Uysal, Mete Sefa, Van Rooy, Dirk, Västfjäll, Daniel, Vieira, Joana B., von Sikorski, Christian, Walker, Alexander C., Watermeyer, Jennifer, Willardt, Robin, Wohl, Michael J. A., Wójcik, Adrian Dominik, Wu, Kaidi, Yamada, Yuki, Yilmaz, Onurcan, Yogeeswaran, Kumar, Ziemer, Carolin-Theresa, Zwaan, Rolf A., Boggio, Paulo Sergio, Whillans, Ashley, Van Lange, Paul A .M., Prasad, Rajib, Onderco, Michal, O'Madagain, Cathal, Nesh-Nash, Tarik, Laguna, Oscar Moreda, Kubin, Emily, Gümren, Mert, Fenwick, Ali, Ertan, Arhan S., Bernstein, Michael J., Amara, Hanane and Van Bavel, Jay Joseph (2022) Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS Nexus, 1 (3) , pgac093. pp. 1-15. ISSN 2752-6542 [Article] (doi:10.1093/pnasnexus/pgac093)

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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.

Item Type: Article
Keywords (uncontrolled): COVID-19, social distancing, hygiene, policy support, public health measures
Research Areas: A. > Business School > Economics
Item ID: 35407
Notes on copyright: © The Author(s) 2022. Published by Oxford University Press on behalf of the National Academy of Sciences.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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Depositing User: Jisc Publications Router
Date Deposited: 14 Jul 2022 17:34
Last Modified: 28 Sep 2022 04:39
URI: https://eprints.mdx.ac.uk/id/eprint/35407

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