What Is Said, Who Says It, and How It Spreads: A Socio-Semantic Graph Framework for Fake News Detection

What Is Said, Who Says It, and How It Spreads: A Socio-Semantic Graph Framework for Fake News Detection | 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 Research Article What Is Said, Who Says It, and How It Spreads: A Socio-Semantic Graph Framework for Fake News Detection Ilhem Salah, Tahar Berradia, Khaled Jouini, Adnane Cabani, Ouajdi Korbaa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7482447/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The proliferation of misinformation on social media poses a critical challenge. Existing detection approaches often rely on isolated signals like content, propagation structure, or user credibility. However, these signals are ambiguous in isolation: propagation patterns require semantic interpretation, while source credibility must be contextualized by the specific claims being made. Efficient veracity detection therefore lies in modeling their complex interplay. We address this with a unified socio-semantic graph framework that jointly models what is said, who says it, and how it spreads. Our model represents conversational cascades as attributed graphs, enriched with semantic embeddings and a novel behavioral reputation score that penalizes visibility amplification. Veracity is assessed through the credibility-weighted consensus that emerges from these dynamics. Experiments on the RumourEval 2019 benchmark demonstrate our approach's effectiveness, achieving a macro F1-score of 63.55%, which compares favorably to state-of-the-art methods. Our work lays the groundwork for scalable credibility analysis in large-scale environments. Fake News Detection Attributed Graphs Behavioral Credibility Socio-Semantic Modeling Social Network Analysis Graph Processing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 May, 2026 Reviewers agreed at journal 20 May, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviews received at journal 27 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 28 Aug, 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-7482447","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517052681,"identity":"6b7dfe10-8758-4e98-9db7-4d4c446e5ab5","order_by":0,"name":"Ilhem Salah","email":"","orcid":"","institution":"University of Sousse","correspondingAuthor":false,"prefix":"","firstName":"Ilhem","middleName":"","lastName":"Salah","suffix":""},{"id":517052682,"identity":"63e330a2-62fe-4437-b79a-154993d2f303","order_by":1,"name":"Tahar Berradia","email":"","orcid":"","institution":"University of 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