Conspiracist attributes differentiate pro- and anti-vaccine online discourses about data

Conspiracist attributes differentiate pro- and anti-vaccine online discourses about data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Conspiracist attributes differentiate pro- and anti-vaccine online discourses about data Ruijia Cheng, Aaron Shaw, Benjamin Mako Hill This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9230846/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The public frequently and increasingly participates in discussions about vaccines and data on social media. However, such public engagement often includes claims and rumors that misuse data to support anti-vaccine perspectives and lead to negative public health impacts. Often, people expressing anti-vaccine views utilize the same rhetorical strategies as vaccine advocates to engage with data and evidence. Prior work finds that machine learning systems can distinguish pro- and anti-vaccine content as well as other types of misinformation and conspiracy theories. However, such approaches do not provide interpretable insights that explain these differences. We propose a theory-driven approach to distinguish anti-vaccine social media discussions of data by identifying textual characteristics of conspiracy discourses, including mentions of authority figures, expressions of certainty, and causal claims. We conduct statistical analyses to evaluate this approach using a dataset of public Twitter posts containing pro- and anti-vaccine discourse on data about COVID-19 vaccines during the first six months after they became available. Compared to pro-vaccine data discourses on Twitter, we find that anti-vaccine data discourses are less likely to mention authority figures but more likely to express certainty and make causal claims. These findings provide insights into salient attributes of anti-vaccine data discourses that may explain the success of prior machine learning approaches. The findings can inform ongoing vaccination and public health interventions. Humanities/Cultural and media studies Social science/Cultural and media studies Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 04 May, 2026 Editor invited by journal 14 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9230846","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":635735571,"identity":"f459fa09-2e1b-45c6-821f-85da1d6df1bc","order_by":0,"name":"Ruijia Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYFACHhBhI4dgE6klzZhkLYcSG4jWYt5+9uDjgl8H0jfcSGB88LaNCC0yZ/KSjWf23ckFamE2nEuMFgkJHjNp3p5nuRtuJ7BJ8xKpxfw3b8/hdIPbCey/idVixszz43ACUAsbM3FaeHKMpXkb0gxn3n/YLDnnHDFa2M8Yfub5YyPPd+bwwQ9vyojQAgaMYPcwNhCrHgT+kKJ4FIyCUTAKRhwAADVWNOngkCUiAAAAAElFTkSuQmCC","orcid":"","institution":"University of Washington","correspondingAuthor":true,"prefix":"","firstName":"Ruijia","middleName":"","lastName":"Cheng","suffix":""},{"id":635735572,"identity":"107556d5-b986-46df-94bf-026a4307773d","order_by":1,"name":"Aaron Shaw","email":"","orcid":"","institution":"Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"","lastName":"Shaw","suffix":""},{"id":635735573,"identity":"21416913-7176-4389-8071-8d0a6676fb61","order_by":2,"name":"Benjamin Mako Hill","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"Mako","lastName":"Hill","suffix":""}],"badges":[],"createdAt":"2026-03-26 07:42:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9230846/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9230846/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109068287,"identity":"00f0d029-e39a-4557-8dfe-f52e9f013cac","added_by":"auto","created_at":"2026-05-12 10:05:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":170579,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsCovidcriticaldataliteracies3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9230846/v1_covered_a7547d30-6a80-49e1-ae43-0c9e02a84eba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conspiracist attributes differentiate pro- and anti-vaccine online discourses about data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9230846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9230846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The public frequently and increasingly participates in discussions about vaccines and data on social media. However, such public engagement often includes claims and rumors that misuse data to support anti-vaccine perspectives and lead to negative public health impacts. Often, people expressing anti-vaccine views utilize the same rhetorical strategies as vaccine advocates to engage with data and evidence. Prior work finds that machine learning systems can distinguish pro- and anti-vaccine content as well as other types of misinformation and conspiracy theories. However, such approaches do not provide interpretable insights that explain these differences. We propose a theory-driven approach to distinguish anti-vaccine social media discussions of data by identifying textual characteristics of conspiracy discourses, including mentions of authority figures, expressions of certainty, and causal claims. We conduct statistical analyses to evaluate this approach using a dataset of public Twitter posts containing pro- and anti-vaccine discourse on data about COVID-19 vaccines during the first six months after they became available. Compared to pro-vaccine data discourses on Twitter, we find that anti-vaccine data discourses are less likely to mention authority figures but more likely to express certainty and make causal claims. These findings provide insights into salient attributes of anti-vaccine data discourses that may explain the success of prior machine learning approaches. The findings can inform ongoing vaccination and public health interventions. 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