The Topology of Truth: Structural Asymmetries in the Spread of Fact and Falsehood

The Topology of Truth: Structural Asymmetries in the Spread of Fact and Falsehood | 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 The Topology of Truth: Structural Asymmetries in the Spread of Fact and Falsehood Rakesh Sengupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8778938/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 Despite substantial efforts to combat online misinformation, corrections often fail to reach affected audiences. Here we propose that this persistence stems from a fundamental topological asymmetry in how truth and falsehood spread. Analyzing 2.1 million diffusion cascades from the 2016 US election, we identify two distinct regimes of network fracture. Misinformation exhibits 'Viral Fracture' ($\sigma^2=560$), defined by intermediate modularity ($Q \approx 0.40$) and high local clustering ($C=0.37$). Its cascade distribution follows a Log-Normal law ($R=-0.098$,$p=0.016$ ), confirming multiplicative community amplification. This topology corresponds to the theoretical 'Peak Fracture' zone, where semi-permeable communities act as bridges that maximize systemic volatility. In contrast, fact-checking exhibits 'Broadcast Fracture' ($\sigma^2=659$), defined by hyper-segregation ($Q \approx 0.68$) and low clustering ($C=0.14$). Truth is effectively siloed in high-modularity clusters, unable to cross the structural fissures of the network. Simulations confirm that this asymmetry confers a survival advantage: the mesh topology of misinformation acts as an incubation chamber, making it $2.3\times$ more likely to reach viral scale than the fragile star topology of fact-checking. We conclude that the failure of debunking is structural: truth attempts to penetrate a resilient, bridging mesh using a brittle, segregated signal. Epistemic resilience requires 'viral truth' - interventions that artificially lower the modularity of factual networks to bridge the topological gap. Social science/Complex networks Social science/Psychology/Human behaviour Misinformation Social Network Analysis Log-Normal Distribution Information Diffusion Full Text Additional Declarations There is NO Competing Interest. 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-8778938","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590965320,"identity":"659d80f6-3792-41e1-9fba-e6cd6060e3df","order_by":0,"name":"Rakesh Sengupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYNCCAwwMBuwNQIaBBSlaeA6AtEiQokUiAcQiQgvf+QVsH36csbE3l3x+dcOPAgkG/vbuBLxaJG88YJ7ZcyMtcefsnLKbPUCHSZw5uwGvFoMbB5gZeD4cTjC4nZN2gweoxUAil7AWxj8f/tsb3DyTdvMPUVrONzAz89w4wLjhBvux20TZInmDsZlZ5kxy4oYzOWy3ZQwkeAj6he/84cOMb47Z2RscP/7s5ps/NnL87b34tTDcSGyAsngMwCR+5SBw/gCMxf6AsOpRMApGwSgYkQAAlRlPJ7DE4O0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1768-0551","institution":"Krea University","correspondingAuthor":true,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Sengupta","suffix":""}],"badges":[],"createdAt":"2026-02-03 17:39:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8778938/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8778938/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103049386,"identity":"05deb9cc-d60a-4c8b-82b1-4c6b739b1691","added_by":"auto","created_at":"2026-02-20 07:40:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":424759,"visible":true,"origin":"","legend":"Article File","description":"","filename":"topologyoftruth.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8778938/v1_covered_7a83aadb-876b-4297-8e47-0e0e4ed0a956.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The Topology of Truth: Structural Asymmetries in the Spread of Fact and Falsehood","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Misinformation, Social Network Analysis, Log-Normal Distribution, Information Diffusion","lastPublishedDoi":"10.21203/rs.3.rs-8778938/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8778938/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Despite substantial efforts to combat online misinformation, corrections often fail to reach affected audiences. Here we propose that this persistence stems from a fundamental topological asymmetry in how truth and falsehood spread. Analyzing 2.1 million diffusion cascades from the 2016 US election, we identify two distinct regimes of network fracture. Misinformation exhibits 'Viral Fracture' ($\\sigma^2=560$), defined by intermediate modularity ($Q \\approx 0.40$) and high local clustering ($C=0.37$). Its cascade distribution follows a Log-Normal law ($R=-0.098$,$p=0.016$ ), confirming multiplicative community amplification. This topology corresponds to the theoretical 'Peak Fracture' zone, where semi-permeable communities act as bridges that maximize systemic volatility. In contrast, fact-checking exhibits 'Broadcast Fracture' ($\\sigma^2=659$), defined by hyper-segregation ($Q \\approx 0.68$) and low clustering ($C=0.14$). Truth is effectively siloed in high-modularity clusters, unable to cross the structural fissures of the network. Simulations confirm that this asymmetry confers a survival advantage: the mesh topology of misinformation acts as an incubation chamber, making it $2.3\\times$ more likely to reach viral scale than the fragile star topology of fact-checking. We conclude that the failure of debunking is structural: truth attempts to penetrate a resilient, bridging mesh using a brittle, segregated signal. 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