[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126586-en":3,"doc-seo-126586-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126586,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Identifying important individual-and country-level predictors of conspiracy theorizing - A machine learning analysis","Psychological research has increasingly examined predictors of conspiracy theorizing, yet prior studies often rely on limited sets of variables within few national contexts, making it hard to compare relative importance and to detect overlooked factors. This study applies machine learning to rank 115 individual- and country-level variables using data from 56,072 respondents across 28 countries during the early COVID-19 weeks. Individual predictors include discontent, paranoia, and personal struggle, while country-level predictors emphasize political stability and effective government COVID response.","Received: 6 January 2022  \nAccepted: 30 May 2023  \nDOI: 10.1002/ejsp.2968  \nRESEARCH ARTICLE  \nIdentifying important individual-and country-level predictors of conspiracy theorizing: A machine learning analysis  \nKaren M. Douglas1   Robbie M. Sutton1   Caspar J. Van Lissa2  Wolfgang Stroebe3  Jannis Kreienkamp3  Maximilian Agostini3  Jocelyn J. Bélanger4   Ben Gützkow3  Georgios Abakoumkin5   Jamilah Hanum Abdul Khaiyom6  Vjollca Ahmedi7  HandanAkkas8   \nCarlos A. Almenara9  Mohsin Atta10  Sabahat Cigdem Bagci11   Sima Basel4  \nEdona Berisha Kida7  Allan B. I. Bernardo12  Nicholas R. Buttrick13   \nPhatthanakit Chobthamkit14  Hoon-Seok Choi15  Mioara Cristea16   Sára Csaba17  \nKaja Damnjanovic18  Ivan Danyliuk19  Violeta Enea22  Daiane Gracieli Faller23 Alexandra Gheorghiu22  Ángel Gómez25 Mai Helmy28, 29  Joevarian Hudiyana30 Veljko Jovanović32  Željka Kamenov33  \nArobindu Dash20  Daniela Di Santo21   Gavan Fitzsimons24   \n Ali Hamaidia26  Qing Han27  Bertus F. Jeronimus3  Ding-Yu Jiang31 Anna Kende17   Shian-Ling Keng34   \nTra Thi Thanh Kieu35  Yasin Koc3   Kamila Kovyazina36  Inna Kozytska19 Joshua Krause3  ArieW. Kruglanski37  Anton Kurapov19, 38  Maja Kutlaca39 Nóra Anna Lantos17   Edward P. Lemay Jr.37  Cokorda Bagus Jaya Lesmana40  \nWinnifred R. Louis41 Kira O. McCabe44  Erica Molinario46   \n Adrian Lueders42 Jasmina Mehulić33  \nManuel Moyano47  \nNajma Iqbal Malik10  Anton Martinez43 Mirra Noor Milla30  Idris Mohammed45  Hayat Muhammad48  Silvana Mula49   \nHamdi Muluk30 Boglárka Nyúl17  \nSolomiia Myroniuk3  PaulA. O’Keefe34  \nReza Najafi50  Claudia F. Nisa4 Jose Javier Olivas Osuna51   \nEvgeny N. Osin52  Joonha Park53  Gennaro Pica54   Antonio Pierro21 Jonas Rees55  Anne Margit Reitsema3  Elena Resta21   Marika Rullo56  \nMichelle K. Ryan3, 57  Adil Samekin58 Birga M. Schumpe60  HeylaA. Selim61 Samiah Sultana3  Eleftheria Tseliou5  \nPekka Santtila59  Edyta Sasin4  Michael Vicente Stanton62   \nAkira Utsugi63  Jolien Anne van Breen64   \nKees Van Veen3  Michelle R. vanDellen65  Alexandra Vázquez25  Robin Wollast1  Victoria Wai-LanYeung66  Somayeh Zand67   Iris L. Žeželj18  Bang Zheng68  Andreas Zick55  Claudia Zúñiga69  N. Pontus Leander3, 70  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2023 The Authors. European Journal of Social Psychology published by John Wiley & Sons Ltd.  \nDOUGLAS ET AL.  \nCorrespondence  \nKaren M. Douglas, University of Kent, Canterbury, CT2 7NP, UK. [Email:](Email: K.Douglas@kent.ac.uk)[ K.Douglas@kent.ac.uk](Email: K.Douglas@kent.ac.uk)  \nFunding information  \nNew York University Abu Dhabi, Grant/Award Number: VCDSF/75-71015; University of Groningen (Sustainable Society & Ubbo Emmius Fund); HORIZON EUROPE European Research Council, Grant/Award Number: 101018262  \nAbstract  \nPsychological 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 t","cbCaiiXInNs7iJl3","https://ap.wps.com/l/cbCaiiXInNs7iJl3","pdf",816635,3,1,13,"English","en",105,"# Abstract\n# Introduction\n## What are conspiracy theories and where might they thrive?","[{\"question\":\"How does the study evaluate predictors of conspiracy theorizing?\",\"answer\":\"It uses machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing.\"},{\"question\":\"What data and time period does the analysis use?\",\"answer\":\"Data come from 56,072 respondents across 28 countries collected during the early weeks of the COVID-19 pandemic.\"},{\"question\":\"Which predictors are most important at the individual and country levels?\",\"answer\":\"At the individual level, key predictors include societal discontent, paranoia, and personal struggle. At the country level, important predictors include indicators of political stability and effective government COVID response.\"}]","Identifying important individual-and country-level predictors of conspiracy theorizing - A machine learning analysis | PDF",1785933505,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"identifying-important-individual-and-country-level-predictors-of-conspiracy-theorizing-a-machine-learning-analysis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/identifying-important-individual-and-country-level-predictors-of-conspiracy-theorizing-a-machine-learning-analysis/126586/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the study evaluate predictors of conspiracy theorizing?","Question",{"text":76,"@type":77},"It uses machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data and time period does the analysis use?",{"text":81,"@type":77},"Data come from 56,072 respondents across 28 countries collected during the early weeks of the COVID-19 pandemic.",{"name":83,"@type":74,"acceptedAnswer":84},"Which predictors are most important at the individual and country levels?",{"text":85,"@type":77},"At the individual level, key predictors include societal discontent, paranoia, and personal struggle. 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