[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127416-en":3,"doc-seo-127416-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},127416,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Identifying important individual-and country-level predictors of conspiracy theorizing - A machine learning analysis","Psychological research on the predictors of conspiracy theorizing—explaining important social and political events as secret plots by malevolent groups—has expanded, yet existing studies often test limited sets of predictors within few national contexts. This limits assessment of relative importance and can miss variables that matter. Using machine learning, the study ranks the importance of 115 individual- and country-level predictors. Data from 56,072 respondents across 28 countries during early COVID-19 weeks identify key individual factors and reveal country-level effects tied to political stability and government COVID response.","UvA-DARE (Digital Academic Repository)  \nIdentifying important individual-and country-level predictors of conspiracy theorizing  \nA machine learning analysis  \nDouglas, K. M. ; Sutton, R. M. ; Van Lissa, C.J. ; Stroebe, W. ; Kreienkamp, J. ; Kruglanski, A.W. ; Schumpe, B. M. ; PsyCorona  \nDOI  \n10.1002/ejsp.2968  \nPublication date  \n2023  \nDocument Version  \nFinal published version  \nPublished in  \nEuropean Journal of Social Psychology  \nLicense  \nCC BY  \nLink to publication  \nCitation for published version (APA):  \nDouglas, K. M. , Sutton, R. M. , Van Lissa, C. J. , Stroebe, W. , Kreienkamp, J. , Kruglanski, A. W. , Schumpe, B. M. , & PsyCorona (2023) . Identifying important individual-and country-level predictors of conspiracy theorizing: A machine learning analysis. European Journal of Social  \nPsychology, 53(6), 1191-1203 . [https://doi.org/10.1002/ejsp.2968](https://doi.org/10.1002/ejsp.2968)  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:07 Jan 2026  \nReceived: 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  Alexandr","cbCaifdD9qfA9knu","https://ap.wps.com/l/cbCaifdD9qfA9knu","pdf",1002282,1,14,"English","en",105,"# Abstract\n## Predictors of conspiracy theorizing\n## Machine learning approach\n## Data and context (COVID-19)\n## Key individual-level and country-level findings","[{\"question\":\"Why do traditional studies struggle to identify the most important predictors of conspiracy theorizing?\",\"answer\":\"They usually examine only a small number of predictors and focus on one or a few national contexts, making it hard to compare predictor importance and potentially overlooking relevant variables.\"},{\"question\":\"What method and outcome did the study use to evaluate predictors?\",\"answer\":\"The study applied machine learning to rank-order the importance of 115 individual- and country-level variables for predicting conspiracy theorizing.\"},{\"question\":\"What individual-level factors and what country-level factors were reported as important?\",\"answer\":\"Important individual-level predictors included societal discontent, paranoia, and personal struggle. Important country-level predictors included indicators of political stability and effective government COVID-19 response.\"}]","Identifying important individual-and country-level predictors of conspiracy theorizing - A machine learning analysis | PDF",1785938773,35,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identifying-important-individual-and-country-level-predictors-of-conspiracy-theorizing-a-machine-learning-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@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/127416/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do traditional studies struggle to identify the most important predictors of conspiracy theorizing?","Question",{"text":75,"@type":76},"They usually examine only a small number of predictors and focus on one or a few national contexts, making it hard to compare predictor importance and potentially overlooking relevant variables.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What method and outcome did the study use to evaluate predictors?",{"text":80,"@type":76},"The study applied machine learning to rank-order the importance of 115 individual- and country-level variables for predicting conspiracy theorizing.",{"name":82,"@type":73,"acceptedAnswer":83},"What individual-level factors and what country-level factors were reported as important?",{"text":84,"@type":76},"Important individual-level predictors included societal discontent, paranoia, and personal struggle. 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