Identifying important individual- and country-level predictors of conspiracy theorizing: A machine learning analysis

• Autores: Douglas, Karen M., Sutton, Robbie M., van Lissa, Caspar J., Stroebe, Wolfgang, Kreienkamp, Jannis, Agostini, Maximilian, Bélanger, Jocelyn J., Gützkow, Ben, Abakoumkin, Georgios, Khaiyom, Jamilah Hanum Abdul, Ahmedi, Vjollca, Akkas, Handan, Almenara, Carlos A., Atta, Mohsin, Bagci, Sabahat Cigdem, Basel, Sima, Berisha Kida, Edona, Bernardo, Allan B.I., Buttrick, Nicholas R., Chobthamkit, Phatthanakit, Choi, Hoon Seok, Cristea, Mioara, Csaba, Sára, Damnjanovic, Kaja, Danyliuk, Ivan, Dash, Arobindu, Di Santo, Daniela, Enea, Violeta, Faller, Daiane Gracieli, Fitzsimons, Gavan, Gheorghiu, Alexandra, Gómez, Ángel, Hamaidia, Ali, Han, Qing, Helmy, Mai, Hudiyana, Joevarian, Jeronimus, Bertus F., Yu Jiang, Ding, Jovanović, Veljko, Kamenov, Željka, Kende, Anna, Keng, Shian Ling, Kieu, Tra Thi Thanh, Koc, Yasin, Kovyazina, Kamila, Kozytska, Inna, Krause, Joshua, Kruglanski, Arie W., Kurapov, Anton, Kutlaca, Maja, Lantos, Nóra Anna, Lemay, Edward P., Lesmana, Cokorda Bagus Jaya, Louis, Winnifred R., Lueders, Adrian, Malik, Najma Iqbal, Martinez, Anton, McCabe, Kira O., Mehulić, Jasmina, Milla, Mirra Noor, Mohammed, Idris, Molinario, Erica, Moyano, Manuel, Muhammad, Hayat, Mula, Silvana, Muluk, Hamdi, Myroniuk, Solomiia, Najafi, Reza, Nisa, Claudia F., Nyúl, Boglárka, O'Keefe, Paul A., Olivas Osuna, Jose Javier, Osin, Evgeny N., Park, Joonha, Pica, Gennaro, Pierro, Antonio, Rees, Jonas, Reitsema, Anne Margit, Resta, Elena, Rullo, Marika, Ryan, Michelle K., Samekin, Adil, Santtila, Pekka, Sasin, Edyta, Schumpe, Birga M., Selim, Heyla A., Stanton, Michael Vicente, Sultana, Samiah, Tseliou, Eleftheria, Utsugi, Akira, van Breen, Jolien Anne, van Veen, Kees, vanDellen, Michelle R., Vázquez, Alexandra, Wollast, Robin, Yeung, Victoria Wai Lan, Zand, Somayeh, Žeželj, Iris L., Zheng, Bang, Zick, Andreas
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/668524
• Enlace del recurso: http://hdl.handle.net/10757/668524
• Nivel de acceso: acceso abierto
• Materia: conspiracy theories; country-level variables; COVID-19; individual-level variables; machine learning; Psychological research; Conspiracy theorizing; Predictors; National contexts; Machine learning; Individual- and country-level variables; COVID-19 pandemic; Societal discontent; Political stability; Effective government COVID response

Psychological research on the predictors of conspiracy theorizing—explaining important social and political events or circumstances as secret plots by malevolent groups—has flourished in recent years. However, research has typically examined only a small number of predictors in one, or a small number of, national contexts. Such approaches make it difficult to examine the relative importance of predictors, and risk overlooking some potentially relevant variables altogether. To overcome this limitation, the present study used machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing. Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic. Echoing previous findings, important predictors at the individual level included societal discontent, paranoia, and personal struggle. Contrary to prior research, important country-level predictors included indicators of political stability and effective government COVID response, which suggests that conspiracy theorizing may thrive in relatively well-functioning democracies.