Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning (2024)

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 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 endorsem*nt 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.

Originalsprache	Englisch
Aufsatznummer	pgac093
Fachzeitschrift	PNAS nexus
Jahrgang	1
Ausgabenummer	3
DOIs	https://doi.org/10.1093/pnasnexus/pgac093
Publikationsstatus	Veröffentlicht - Juli 2022

ÖFOS 2012

501030 Kognitionswissenschaft
501006 Experimentalpsychologie

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

Zugriff auf Dokument

10.1093/pnasnexus/pgac093

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Pavlović, T., Azevedo, F., De, K., Riaño-Moreno, J. C., Maglić, M., Gkinopoulos, T., Donnelly-Kehoe, P. A., Payán-Gómez, C., Huang, G., Kantorowicz, J., Birtel, M. D., Schönegger, P., Capraro, V., Santamaría-García, H., Yucel, M., Ibanez, A., Rathje, S., Wetter, E., Stanojević, D., ... Van Bavel, J. J. (2022). Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS nexus, 1(3), [pgac093]. https://doi.org/10.1093/pnasnexus/pgac093

Pavlović, Tomislav ; Azevedo, Flavio ; De, Koustav et al. / Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. in: PNAS nexus. 2022 ; Band 1, Nr. 3.

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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 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 endorsem*nt 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.",

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author = "Tomislav Pavlovi{\'c} and Flavio Azevedo and Koustav De and Ria{\~n}o-Moreno, {Juli{\'a}n C} and Marina Magli{\'c} and Theofilos Gkinopoulos and Donnelly-Kehoe, {Patricio Andreas} and C{\'e}sar Pay{\'a}n-G{\'o}mez and Guanxiong Huang and Jaroslaw Kantorowicz and Birtel, {Mich{\`e}le D} and Philipp Sch{\"o}negger and Valerio Capraro and Hernando Santamar{\'i}a-Garc{\'i}a and Meltem Yucel and Agustin Ibanez and Steve Rathje and Erik Wetter and Dragan Stanojevi{\'c} and {van Prooijen}, Jan-Willem and Eugenia Hesse and Elbaek, {Christian T} and Renata Franc and Zoran Pavlovi{\'c} and Panagiotis Mitkidis and Aleksandra Cichocka and Michele Gelfand and Mark Alfano and Ross, {Robert M} and Hallgeir Sj{\aa}stad and Nezlek, {John B} and Aleksandra Cislak and Patricia Lockwood and Koen Abts and Elena Agadullina and Amodio, {David M} and Apps, {Matthew A J} and Aruta, {John Jamir Benzon} and Sahba Besharati and Alexander Bor and Becky Choma and William Cunningham and Waqas Ejaz and 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note = "{\textcopyright} The Author(s) 2022. Published by Oxford University Press on behalf of National Academy of Sciences.",

year = "2022",

month = jul,

doi = "10.1093/pnasnexus/pgac093",

language = "English",

volume = "1",

journal = "PNAS nexus",

issn = "2752-6542",

publisher = "Oxford University Press",

number = "3",

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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, co*ckcroft, 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, Di Paolo, R, 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, 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, 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, Kubin, E, Gümren, M, Fenwick, A, Ertan, AS, Bernstein, MJ, Amara, H & Van Bavel, JJ 2022, 'Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning', PNAS nexus, Jg. 1, Nr. 3, pgac093. https://doi.org/10.1093/pnasnexus/pgac093

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. / Pavlović, Tomislav; Azevedo, Flavio; De, Koustav et al.

in: PNAS nexus, Band 1, Nr. 3, pgac093, 07.2022.

Veröffentlichungen: Beitrag in Fachzeitschrift › Artikel › Peer Reviewed

TY - JOUR

T1 - Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

AU - Pavlović, Tomislav

AU - Azevedo, Flavio

AU - De, Koustav

AU - Riaño-Moreno, Julián C

AU - Maglić, Marina

AU - Gkinopoulos, Theofilos

AU - Donnelly-Kehoe, Patricio Andreas

AU - Payán-Gómez, César

AU - Huang, Guanxiong

AU - Kantorowicz, Jaroslaw

AU - Birtel, Michèle D

AU - Schönegger, Philipp

AU - Capraro, Valerio

AU - Santamaría-García, Hernando

AU - Yucel, Meltem

AU - Ibanez, Agustin

AU - Rathje, Steve

AU - Wetter, Erik

AU - Stanojević, Dragan

AU - van Prooijen, Jan-Willem

AU - Hesse, Eugenia

AU - Elbaek, Christian T

AU - Franc, Renata

AU - Pavlović, Zoran

AU - Mitkidis, Panagiotis

AU - Cichocka, Aleksandra

AU - Gelfand, Michele

AU - Alfano, Mark

AU - Ross, Robert M

AU - Sjåstad, Hallgeir

AU - Nezlek, John B

AU - Cislak, Aleksandra

AU - Lockwood, Patricia

AU - Abts, Koen

AU - Agadullina, Elena

AU - Amodio, David M

AU - Apps, Matthew A J

AU - Aruta, John Jamir Benzon

AU - Besharati, Sahba

AU - Bor, Alexander

AU - Choma, Becky

AU - Cunningham, William

AU - Ejaz, Waqas

AU - Farmer, Harry

AU - Findor, Andrej

AU - Gjoneska, Biljana

AU - Gualda, Estrella

AU - Huynh, Toan L D

AU - Imran, Mostak Ahamed

AU - Israelashvili, Jacob

AU - Kantorowicz-Reznichenko, Elena

AU - Krouwel, André

AU - Kutiyski, Yordan

AU - Laakasuo, Michael

AU - Lamm, Claus

AU - Levy, Jonathan

AU - Leygue, Caroline

AU - Lin, Ming-Jen

AU - Mansoor, Mohammad Sabbir

AU - Marie, Antoine

AU - Mayiwar, Lewend

AU - Mazepus, Honorata

AU - McHugh, Cillian

AU - Olsson, Andreas

AU - Otterbring, Tobias

AU - Packer, Dominic

AU - Palomäki, Jussi

AU - Perry, Anat

AU - Petersen, Michael Bang

AU - Puthillam, Arathy

AU - Rothmund, Tobias

AU - Schmid, Petra C

AU - Stadelmann, David

AU - Stoica, Augustin

AU - Stoyanov, Drozdstoy

AU - Stoyanova, Kristina

AU - Tewari, Shruti

AU - Todosijević, Bojan

AU - Torgler, Benno

AU - Tsakiris, Manos

AU - Tung, Hans H

AU - Umbreș, Radu Gabriel

AU - Vanags, Edmunds

AU - Vlasceanu, Madalina

AU - Vonasch, Andrew J

AU - Zhang, Yucheng

AU - Abad, Mohcine

AU - Adler, Eli

AU - Mdarhri, Hamza Alaoui

AU - Antazo, Benedict

AU - Ay, F Ceren

AU - Ba, Mouhamadou El Hady

AU - Barbosa, Sergio

AU - Bastian, Brock

AU - Berg, Anton

AU - Białek, Michał

AU - Bilancini, Ennio

AU - Bogatyreva, Natalia

AU - Boncinelli, Leonardo

AU - Booth, Jonathan E

AU - Borau, Sylvie

AU - Buchel, Ondrej

AU - de Carvalho, Chrissie Ferreira

AU - Celadin, Tatiana

AU - Cerami, Chiara

AU - Chalise, Hom Nath

AU - Cheng, Xiaojun

AU - Cian, Luca

AU - co*ckcroft, Kate

AU - Conway, Jane

AU - Córdoba-Delgado, Mateo A

AU - Crespi, Chiara

AU - Crouzevialle, Marie

AU - Cutler, Jo

AU - Cypryańska, Marzena

AU - Dabrowska, Justyna

AU - Davis, Victoria H

AU - Minda, John Paul

AU - Dayley, Pamala N

AU - Delouvée, Sylvain

AU - Denkovski, Ognjan

AU - Dezecache, Guillaume

AU - Dhaliwal, Nathan A

AU - Diato, Alelie

AU - Di Paolo, Roberto

AU - Dulleck, Uwe

AU - Ekmanis, Jānis

AU - Etienne, Tom W

AU - Farhana, Hapsa Hossain

AU - Farkhari, Fahima

AU - Fidanovski, Kristijan

AU - Flew, Terry

AU - Fraser, Shona

AU - Frempong, Raymond Boadi

AU - Fugelsang, Jonathan

AU - Gale, Jessica

AU - García-Navarro, E Begoña

AU - Garladinne, Prasad

AU - Gray, Kurt

AU - Griffin, Siobhán M

AU - Gronfeldt, Bjarki

AU - Gruber, June

AU - Halperin, Eran

AU - Herzon, Volo

AU - Hruška, Matej

AU - Hudecek, Matthias F C

AU - Isler, Ozan

AU - Jangard, Simon

AU - Jørgensen, Frederik

AU - Keudel, Oleksandra

AU - Koppel, Lina

AU - Koverola, Mika

AU - Kunnari, Anton

AU - Leota, Josh

AU - Lermer, Eva

AU - Li, Chunyun

AU - Longoni, Chiara

AU - McCashin, Darragh

AU - Mikloušić, Igor

AU - Molina-Paredes, Juliana

AU - Monroy-Fonseca, César

AU - Morales-Marente, Elena

AU - Moreau, David

AU - Muda, Rafał

AU - Myer, Annalisa

AU - Nash, Kyle

AU - Nitschke, Jonas P

AU - Nurse, Matthew S

AU - de Mello, Victoria Oldemburgo

AU - Palacios-Galvez, Maria Soledad

AU - Pan, Yafeng

AU - Papp, Zsófia

AU - Pärnamets, Philip

AU - Paruzel-Czachura, Mariola

AU - Perander, Silva

AU - Pitman, Michael

AU - Raza, Ali

AU - Rêgo, Gabriel Gaudencio

AU - Robertson, Claire

AU - Rodríguez-Pascual, Iván

AU - Saikkonen, Teemu

AU - Salvador-Ginez, Octavio

AU - Sampaio, Waldir M

AU - Santi, Gaia Chiara

AU - Schultner, David

AU - Schutte, Enid

AU - Scott, Andy

AU - Skali, Ahmed

AU - Stefaniak, Anna

AU - Sternisko, Anni

AU - Strickland, Brent

AU - Thomas, Jeffrey P

AU - Tinghög, Gustav

AU - Traast, Iris J

AU - Tucciarelli, Raffaele

AU - Tyrala, Michael

AU - Ungson, Nick D

AU - Uysal, Mete Sefa

AU - Van Rooy, Dirk

AU - Västfjäll, Daniel

AU - Vieira, Joana B

AU - von Sikorski, Christian

AU - Walker, Alexander C

AU - Watermeyer, Jennifer

AU - Willardt, Robin

AU - Wohl, Michael J A

AU - Wójcik, Adrian Dominik

AU - Wu, Kaidi

AU - Yamada, Yuki

AU - Yilmaz, Onurcan

AU - Yogeeswaran, Kumar

AU - Ziemer, Carolin-Theresa

AU - Zwaan, Rolf A

AU - Boggio, Paulo Sergio

AU - Whillans, Ashley

AU - Van Lange, Paul A M

AU - Prasad, Rajib

AU - Onderco, Michal

AU - O'Madagain, Cathal

AU - Nesh-Nash, Tarik

AU - Laguna, Oscar Moreda

AU - Kubin, Emily

AU - Gümren, Mert

AU - Fenwick, Ali

AU - Ertan, Arhan S

AU - Bernstein, Michael J

AU - Amara, Hanane

AU - Van Bavel, Jay Joseph

PY - 2022/7

Y1 - 2022/7

N2 - 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 endorsem*nt 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.

AB - 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 endorsem*nt 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.

KW - COVID-19

KW - hygiene

KW - policy support

KW - public health measures

KW - social distancing

UR - http://www.scopus.com/inward/record.url?scp=85137690279&partnerID=8YFLogxK

U2 - 10.1093/pnasnexus/pgac093

DO - 10.1093/pnasnexus/pgac093

M3 - Article

C2 - 35990802

VL - 1

JO - PNAS nexus

JF - PNAS nexus

SN - 2752-6542

IS - 3

M1 - pgac093

ER -

Pavlović T, Azevedo F, De K, Riaño-Moreno JC, Maglić M, Gkinopoulos T et al. Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS nexus. 2022 Jul;1(3):pgac093. doi: 10.1093/pnasnexus/pgac093