Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
Authors: 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 , 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 , Paolo Roberto Di , 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 , Palomäki Jussi , 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 , 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 , Kutiyski Yordan , Kubin Emily , Gümren Mert , Fenwick Ali , Ertan Arhan S , Bernstein Michael J , Amara Hanane , Van Bavel Jay Joseph 
Published: July 2022

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.