Monitoring Social Networking Platforms to Detect and Filter Fake News using Ensemble Learning

Monitoring Social Networking Platforms to Detect and Filter Fake News using Ensemble Learning | 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 Monitoring Social Networking Platforms to Detect and Filter Fake News using Ensemble Learning Khurram Zaheer, Muhammad Ramzan Talib, Muhammad Kashif Hanif, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3832629/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 Social networking platforms and news blogs are providing information to the public. Different business, political, and educational communities rely on these news sources for strategic decision-making. It is straightforward to quickly manipulate and spread real digital news to spread misinformation among communities to get a few benefits or relief. Therefore, an automated system is vital that can detect fake news early during monitoring before it is published online. Several studies have been conducted to detect fake news, focusing on resource-rich languages (mostly English). Because of a lack of annotated corpora, resource-poor languages such as Urdu have not been studied. The objective of this study is to provide an effective method for fake news detection from social media platforms in Urdu. Therefore, in this study, we propose a four-level methodology and perform extensive experiments to find out the best model for fake news detection from social media contents in Urdu. This study proposes a public corpus of Urdu news articles and a methodology for detecting early Urdu fake news. We apply eight machine learning and ensemble learning techniques to three Urdu news corpora. Our experiments show that Bagging with Decision Tree as base learner outperforms the others and obtained F-measure scores of 80.9% on UFN, 84.2% on BET, and 86.02% on FNAC. Fake news detection Social network monitoring Ensemble Learning Natural language processing Urdu language 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-3832629","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265129326,"identity":"1d8e0477-7fae-4d26-9335-b8e184d83d3b","order_by":0,"name":"Khurram Zaheer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACCQk4C0RUSMiBqAMPiNdyxsYYrCWBaC2MbWmJDSAOPi2Ss3vMPlfUbLPnn9188HEF2+H0+WGHHwJtsZPTbcCuRVrmjPHMM8duM0vcOZZseIbncO7G22kGQC3JxmYHsGuRk8gxZmxgu83GcCPHTLJBAqhldgJIy4HEbXi1/LvNI38j//vPBoPD6Yaz0z/g1SIN0tLYdlvC4EYOG2NDQlqCvHQOflskZ6QVMzb23TYwvJFmLNlwwMZwg3ROwYEEA9x+kbiRvJmx4dtte7kbyQ8/Nv6TkJefnb75w4cKOzlcWjCBAVilAbHKQUC+gRTVo2AUjIJRMBIAABDHYtBpPzYFAAAAAElFTkSuQmCC","orcid":"","institution":"Government College University Faisalabad","correspondingAuthor":true,"prefix":"","firstName":"Khurram","middleName":"","lastName":"Zaheer","suffix":""},{"id":265129327,"identity":"98e55c9d-5d94-4e81-88a9-ccea5afd5000","order_by":1,"name":"Muhammad Ramzan Talib","email":"","orcid":"","institution":"Government College University Faisalabad","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Ramzan","lastName":"Talib","suffix":""},{"id":265129328,"identity":"1b5939af-f7de-4c18-a231-c6b2c942fff9","order_by":2,"name":"Muhammad Kashif Hanif","email":"","orcid":"","institution":"Government College University Faisalabad","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Kashif","lastName":"Hanif","suffix":""},{"id":265129329,"identity":"e7c84540-6cfa-4f34-ba0d-13c83a9acd0b","order_by":3,"name":"Muhammad Umer Sarwar","email":"","orcid":"","institution":"Government College University Faisalabad","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Umer","lastName":"Sarwar","suffix":""}],"badges":[],"createdAt":"2024-01-03 19:31:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3832629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3832629/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72341638,"identity":"28eae488-c15c-4517-95d3-db7b21486405","added_by":"auto","created_at":"2024-12-25 19:01:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":990292,"visible":true,"origin":"","legend":"","description":"","filename":"SocialNetworkingandMining.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3832629/v1_covered_f06a567e-975f-442e-acc4-6820dfa0e361.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Monitoring Social Networking Platforms to Detect and Filter Fake News using Ensemble Learning","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":"Fake news detection, Social network monitoring, Ensemble Learning, Natural language processing, Urdu language","lastPublishedDoi":"10.21203/rs.3.rs-3832629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3832629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial networking platforms and news blogs are providing information to the public. Different business, political, and educational communities rely on these news sources for strategic decision-making. It is straightforward to quickly manipulate and spread real digital news to spread misinformation among communities to get a few benefits or relief. Therefore, an automated system is vital that can detect fake news early during monitoring before it is published online. Several studies have been conducted to detect fake news, focusing on resource-rich languages (mostly English). Because of a lack of annotated corpora, resource-poor languages such as Urdu have not been studied. The objective of this study is to provide an effective method for fake news detection from social media platforms in Urdu. Therefore, in this study, we propose a four-level methodology and perform extensive experiments to find out the best model for fake news detection from social media contents in Urdu. 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