[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126738-en":3,"doc-seo-126738-105":30,"detail-sidebar-cat-0-en-105":95},{"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},126738,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",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 or circumstances as secret plots by malevolent groups—has expanded, yet has often relied on limited predictors and narrow national contexts. This study applies machine learning to rank the importance of 115 individual- and country-level variables using data from 56,072 respondents across 28 countries in the early COVID-19 weeks. Individual-level predictors included societal discontent, paranoia, and personal struggle.","University of Groningen  \nIdentifying important individual-and country-level predictors of conspiracy theorizing  \nPsyCorona Collaboration  \nPublished in:  \nEuropean Journal of Social Psychology  \nDOI:  \n10.1002/ejsp.2968  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nPsyCorona Collaboration (2023) . Identifying important individual-and country-level predictors of conspiracy theorizing: A machine learning analysis. European Journal of Social Psychology , 53(6), 1191-1203.  \n[https://doi.org/10.1002/ejsp.2968](https://doi.org/10.1002/ejsp.2968)  \nCopyright  \nOther than for strictly personal use, it 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), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 04-08-2026  \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  ","cbCait4Uw42CFN4o","https://ap.wps.com/l/cbCait4Uw42CFN4o","pdf",1087507,1,14,"English","en",105,"# Abstract\n## Predictive framework and limitation of prior research\n## Machine learning ranking approach\n## Data source and scope (28 countries, early COVID-19)\n## Key findings at individual level","[{\"question\":\"What problem does the study address about conspiracy-theorizing research?\",\"answer\":\"Prior work typically examined only a small set of predictors and limited national contexts, making it hard to compare predictor importance and possibly miss relevant variables.\"},{\"question\":\"How does the study measure which predictors matter most?\",\"answer\":\"It uses machine learning to rank-order the importance of 115 variables spanning both individual-level and country-level factors.\"},{\"question\":\"What data and time period are used to train the analysis?\",\"answer\":\"Data come from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic.\"},{\"question\":\"Which individual-level predictors are highlighted by the results?\",\"answer\":\"Key individual-level predictors include societal discontent, paranoia, and personal struggle.\"}]","Identifying important individual-and country-level predictors of conspiracy theorizing - A machine learning analysis | PDF",1785934529,35,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"identifying-important-individual-and-country-level-predictors-of-conspiracy-theorizing-a-machine-learning-analysis","",{"@graph":36,"@context":89},[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/126738/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address about conspiracy-theorizing research?","Question",{"text":75,"@type":76},"Prior work typically examined only a small set of predictors and limited national contexts, making it hard to compare predictor importance and possibly miss relevant variables.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study measure which predictors matter most?",{"text":80,"@type":76},"It uses machine learning to rank-order the importance of 115 variables spanning both individual-level and country-level factors.",{"name":82,"@type":73,"acceptedAnswer":83},"What data and time period are used to train the analysis?",{"text":84,"@type":76},"Data come from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic.",{"name":86,"@type":73,"acceptedAnswer":87},"Which individual-level predictors are highlighted by the results?",{"text":88,"@type":76},"Key individual-level predictors include societal discontent, paranoia, and personal struggle.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]