An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging

An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging | 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 An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging Amit Neil Ramkissoon, Wayne Goodridge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1208481/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 Mobile Adhoc Networks (MANETs) are utilised in a variety of mission critical situations and as such it is important to detect any fake news that exists in such networks. This research combines the power of Veracity, a unique, computational social system with that of Legitimacy, a dedicated ensemble learning technique, to detect Fake News in MANET Messaging. Veracity uses five algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features using computational social intelligence. To validate Veracity, a dataset of publisher credibility-based and message content-based features is generated to predict fake news. To analyse the data features, Legitimacy, a unique ensemble learning prediction model is used. Four analytical methodologies are used to analyse these experimental results. The analysis of the results reports a good performance of the Veracity architecture combined with the Legitimacy model for the task of fake news detection in MANET Messaging. Computational Social System Content Credibility Ensemble Learning Fake News Detection MANET Full Text 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-1208481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":78188769,"identity":"a7d95090-dbdb-4441-aef8-c00da434b022","order_by":0,"name":"Amit Neil Ramkissoon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYFACxgYwJcEM4cpBKAMgPoBTS2MDshZjIrRArZGA8hIb4BI4tPBPO9z+gHGPXbRkO/vjDx8q7qSvnZHAJs1TwCDHdyMBqxaJ24lAhz1Lzp3NzGMmOePMs9xtN0BaDBiMJXFoYQBrOcCcO4+Zh42Zt+0wREuOAUPiBhxa5CFa6oFa2B9/BmpJN4NqqcelxQCi5TDQYQwG0kAtCTAtCQY4tBgCtcxIOHA8d2Yz2C+HDbededhs/cdAwnDmmQdYtcjdTn/w4cOB6twZ54+DQuywvNnx5IM3Z/yxkec7jsP7IIAqJQCOGglsCnEB/gOkqB4Fo2AUjIIRAADYkGUqO/KW1AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2164-3366","institution":"The University of the West Indies Saint Augustine Campus: The University of the West Indies at St Augustine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Amit","middleName":"Neil","lastName":"Ramkissoon","suffix":""},{"id":78188770,"identity":"d0e3ebce-6f6f-4cb6-a720-ca8526f6bb2d","order_by":1,"name":"Wayne Goodridge","email":"","orcid":"","institution":"The University of the West Indies Saint Augustine Campus: The University of the West Indies at St Augustine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wayne","middleName":"","lastName":"Goodridge","suffix":""}],"badges":[],"createdAt":"2021-12-27 16:39:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1208481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1208481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17643641,"identity":"8f7a0c22-0dd8-470c-9cdd-c96c08c85ae7","added_by":"auto","created_at":"2022-01-25 18:27:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":440146,"visible":true,"origin":"","legend":"","description":"","filename":"SNAM2021.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1208481/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1208481/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"Computational Social System, Content, Credibility, Ensemble Learning, Fake News Detection, MANET ","lastPublishedDoi":"10.21203/rs.3.rs-1208481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1208481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMobile Adhoc Networks (MANETs) are utilised in a variety of mission critical situations and as such it is important to detect any fake news that exists in such networks. 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