The visual representation of misinformation in the news

The visual representation of misinformation in the news | 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PDF Case Report The visual representation of misinformation in the news Ahmed Al-Rawi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5283574/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This exploratory descriptive study examines the visual representation of misinformation in the news, partly relying on the automated labels associated with online and broadcast news such as OCR, face sentiments, labels, and logos detection. The findings of the study show that online and TV news organizations show varied amounts of attention in using misinformation-related terms to visually discuss events and issues, for CNN comes ahead in the US followed by MSNBC and Fox News. In terms of online news, The Daily Mail in the UK is the most active in visually covering misinformation. Many news organizations around the world are using the English language visual mark “Fake news” and related ones to fact check or discredit other sources of information. The study identifies other patterns of visual news coverage including sentiments associated with some political leaders and political polarization around issues related to misinformation such as COVID-19. GDLET news visuals misinformation disinformation fake news Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5283574","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":373357853,"identity":"59b79576-660a-4875-a402-8ad91d7d22c0","order_by":0,"name":"Ahmed Al-Rawi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYFAC5gYgISHHwHyAaC2MYC3GDGwJpGlhSGwgWgt/+8G2Dz/bLNLnt/EYMPyoIUKLxJnE5pm9bRK5G47xGDD2HCPGmgOJzQy824Ba5HsMmBnYiNAhf/5hM+PfbRLp8kCHMTP8I0KLwY3EZmagLQkMQIcxM7YRocXwxsNmZtl/EoYbjrEVHOztI0KL3Pnkw4xvztTJy7cxb3zw4xsRWlDAAVI1jIJRMApGwSjAAQDFJTO+T3klrwAAAABJRU5ErkJggg==","orcid":"","institution":"Simon Fraser University","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Al-Rawi","suffix":""}],"badges":[],"createdAt":"2024-10-17 14:23:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5283574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5283574/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71418993,"identity":"b9df4b36-0718-466a-b864-2a01344b2891","added_by":"auto","created_at":"2024-12-14 16:53:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1754570,"visible":true,"origin":"","legend":"","description":"","filename":"Visualrepresentationoffakenewspaper8kwordswithauthors.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5283574/v1_covered_7767f784-b2d6-4268-ac0d-b67e5e3e449c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The visual representation of misinformation in the news","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GDLET, news, visuals, misinformation, disinformation, fake news","lastPublishedDoi":"10.21203/rs.3.rs-5283574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5283574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis exploratory descriptive study examines the visual representation of misinformation in the news, partly relying on the automated labels associated with online and broadcast news such as OCR, face sentiments, labels, and logos detection. 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