[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126599-en":3,"doc-seo-126599-105":30,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126599,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Identifying important individual-and country-level predictors of conspiracy theorizing - A machine learning analysis","Psychological research has expanded on predictors of conspiracy theorizing, yet prior work often tested limited variables within only one or a few national contexts, restricting comparisons of predictor importance and leaving potentially relevant factors unexamined. 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 weeks of the COVID-19 pandemic. Individual predictors include societal discontent, paranoia, and personal struggle.","This is a repository copy of Identifying important individual‐ and country‐level predictors of conspiracy theorizing: a machine learning analysis.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/201150/](https://eprints.whiterose.ac.uk/201150/)  \nVersion: Published Version  \nArticle:  \nDouglas, [K.M. orcid.org/0000-0002-0381-6924](K.M. orcid.org/0000-0002-0381-6924) , Sutton, [R.M. orcid.org/0000-0003-1542-](R.M. orcid.org/0000-0003-1542-)[ ](R.M. orcid.org/0000-0003-1542-)[1716](1716) , Van Lissa, C.J. et al. (99 more authors) (2023) Identifying important individual‐ and country‐level predictors of conspiracy theorizing: a machine learning analysis. European Journal of Social Psychology. ISSN 0046-2772  \n[https://doi.org/10.1002/ejsp.2968](https://doi.org/10.1002/ejsp.2968)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nReceived: 6 January 2022  \nAccepted: 30 May 2023  \nDOI: 10.1002/ejsp.2968  \nRES EARC H ARTIC LE  \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  Handan Akkas8   \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  \nJasmina 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-Lan Yeung66  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 p","cbCaifeLGNZ7a6R2","https://ap.wps.com/l/cbCaifeLGNZ7a6R2","pdf",782416,1,14,"English","en",105,"# Abstract\n# Keywords\n# 1 INTRODUCTION","[{\"question\":\"How does the study differ from earlier research on conspiracy theorizing predictors?\",\"answer\":\"Earlier studies often examined only a small number of predictors in one or a few national contexts. This study ranks the relative importance of 115 individual- and country-level variables across 28 countries using machine learning.\"},{\"question\":\"What data and scope were used to build the predictive model?\",\"answer\":\"Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic.\"},{\"question\":\"Which predictors were most important at the individual and country levels?\",\"answer\":\"At the individual level, key predictors included societal discontent, paranoia, and personal struggle. At the country level, important predictors included political stability and effective government COVID response.\"}]","Identifying important individual-and country-level predictors of conspiracy theorizing - A machine learning analysis | PDF",1785933638,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"identifying-important-individual-and-country-level-predictors-of-conspiracy-theorizing-a-machine-learning-analysis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identifying-important-individual-and-country-level-predictors-of-conspiracy-theorizing-a-machine-learning-analysis/126599/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","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 differ from earlier research on conspiracy theorizing predictors?","Question",{"text":76,"@type":77},"Earlier studies often examined only a small number of predictors in one or a few national contexts. This study ranks the relative importance of 115 individual- and country-level variables across 28 countries using machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data and scope were used to build the predictive model?",{"text":81,"@type":77},"Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic.",{"name":83,"@type":74,"acceptedAnswer":84},"Which predictors were most important at the individual and country levels?",{"text":85,"@type":77},"At the individual level, key predictors included societal discontent, paranoia, and personal struggle. 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