TIDE-MARK: A Temporal Graph Framework forTracking Evolving Communities in Fake News Cascades

TIDE-MARK: A Temporal Graph Framework forTracking Evolving Communities in Fake News Cascades | 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 TIDE-MARK: A Temporal Graph Framework forTracking Evolving Communities in Fake News Cascades Yanfei Ma, Daozheng Qu, Yibo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7548276/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Misinformation proliferates on social media platforms owing to both static and dynamic user populations. The development,amalgamation, or disintegration of communities throughout an information cascade complicates the longitudinal tracking of thesecommunities. Numerous contemporary methodologies either neglect temporal factors or employ static clustering techniques,which do not accommodate dynamic coordination. We propose TIDE-MARK, a methodology designed to identify communitiesinside fake news cascades that exhibit consistency in both structure and temporal dynamics. The methodology encompassesnode embeddings via temporal graph neural networks, prototype-driven clustering, Markov modeling of community transitions,and reinforcement-based refinement. The unified design facilitates consistent and comprehensible community trajectories.Three empirical datasets pertaining to political, entertainment, and health-related fake news are utilized to evaluate TIDE-MARK. The databases include PolitiFact, GossipCop, and ReCOVery. Our model surpasses robust baselines regardingstructural (modularity, conductance) and temporal (adjusted Rand index) measures, supported by consistent effect sizes.Structural research indicates that real news spreads through more scattered and less organized communities, while falsenews propagates through more stable and well interconnected communities. Our objective is to assess the viability ofinterventions by simulating a structure-aware approach that targets important users in nascent communities. The substantialreduction in cascade modularity and spread demonstrated in the results evidences the efficacy of content-neutral mitigationtechniques. TIDE-MARK offers a transparent and privacy-preserving approach for real-time fake news monitoring, emphasizingstructure-aware strategy signals over textual analysis. It establishes a foundation for innovative methods of dynamic communitymonitoring inside complex social systems and features an interpretable architecture that enables ethical application. Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviews received at journal 20 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviewers invited by journal 27 Sep, 2025 Editor assigned by journal 25 Sep, 2025 Editor invited by journal 25 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 11 Sep, 2025 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. 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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-7548276","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513773092,"identity":"e883425e-0ace-47c6-b5a7-87b1fe6e0019","order_by":0,"name":"Yanfei Ma","email":"","orcid":"","institution":"Fairleigh Dickinson University","correspondingAuthor":false,"prefix":"","firstName":"Yanfei","middleName":"","lastName":"Ma","suffix":""},{"id":513773094,"identity":"ed3136f9-cf30-4b50-b3e6-cc8e2856181d","order_by":1,"name":"Daozheng Qu","email":"","orcid":"","institution":"University of Liverpool","correspondingAuthor":false,"prefix":"","firstName":"Daozheng","middleName":"","lastName":"Qu","suffix":""},{"id":513773096,"identity":"f311dde3-39bd-499d-bc8f-c3e0b4287aca","order_by":2,"name":"Yibo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYNACgwMM/Mwka5FsJtGaAyBdxJp//Ozh1zwFd+SMjzMffPCBwSZf3oGQljN5adY8Bs+MzQ6zJRvOYEiz3EjIOrMDOWbGPAaHE7cd5jGT5mE4bGDYQEjL+TcQLZub+b///kOUlhs5xo9BWjYw87ABw/mwgTwBHQz2N96YMc4B+kXiMJuxZI9BmoEBIS2S/TnGH978uSPH33/44YcfFTYG8oQcBgRsUjxwtgEDURHE/PEHMpcYW0bBKBgFo2BkAQDpokDR+nWI2AAAAABJRU5ErkJggg==","orcid":"","institution":"University of California, San Diego","correspondingAuthor":true,"prefix":"","firstName":"Yibo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-09-06 04:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7548276/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7548276/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-35175-4","type":"published","date":"2026-01-09T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100069919,"identity":"d342ee3e-bdc3-459f-a2c8-3d3ada51ad13","added_by":"auto","created_at":"2026-01-12 16:15:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":835529,"visible":true,"origin":"","legend":"","description":"","filename":"TIDEMARKATemporalGraphFrameworkforTrackingEvolvingCommunitiesinFakeNewsCascades2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7548276/v1_covered_cf929732-9247-45f2-8795-ea83a6d97760.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TIDE-MARK: A Temporal Graph Framework forTracking Evolving Communities in Fake News Cascades","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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-7548276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7548276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Misinformation proliferates on social media platforms owing to both static and dynamic user populations. 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