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    Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
    with Tomislav Pavlović, Flavio Azevedo, Koustav De, Julián C. Riaño-Moreno, Marina Maglić, Theofilos Gkinopoulos, Patricio Andreas Donnelly-Kehoe, César Payán-Gómez, Guanxiong Huang, Jaroslaw Kantorowicz, Michèle D. Birtel, Philipp Schönegger, Valerio Capraro, Hernando Santamaría-García, Meltem Yucel, Agustin Ibanez, Steve Rathje, Erik Wetter, Dragan Stanojević, Jan-Willem van Prooijen, Eugenia Hesse, Christian T. Elbaek, Renata Franc, Zoran Pavlović, Panagiotis Mitkidis, Aleksandra Cichocka, Michele Gelfand, Mark Alfano, Robert M. Ross, John B. Nezlek, Aleksandra Cislak, Patricia Lockwood, Koen Abts, Elena Agadullina, David M. Amodio, Matthew A. J. Apps, John Jamir Benzon Aruta, Sahba Besharati, Alexander Bor, Becky Choma, William Cunningham, Waqas Ejaz, Harry Farmer, Andrej Findor, Biljana Gjoneska, Estrella Gualda, Toan L. D. Huynh, Mostak Ahamed Imran, Jacob Israelashvili, and Elena Kantorowicz-Reznichenko
    Proceedings of the National Academy of Sciences: Nexus. forthcoming.
    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 …Read more