Unveiling Temporal Patterns in Information for Improved Rumor Detection

Unveiling Temporal Patterns in Information for Improved Rumor 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 Unveiling Temporal Patterns in Information for Improved Rumor Detection Omel Mairaj, Shafiq Ur Rehman Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4893251/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2025 Read the published version in Social Network Analysis and Mining → Version 1 posted 7 You are reading this latest preprint version Abstract Rumor detection is a critical task for addressing the spread of misinformation and maintaining the credibility of information sources. Natural Language Processing (NLP) techniques have been employed to propose efficient and effective methods for rumor detection. In the wake of the widespread COVID-19 pandemic, the world has faced extensive strain on health, economics, and social structures. The dissemination of false or inaccurate information on social media, whether intentionally malicious or unintentional, has had detrimental consequences for individuals and society, particularly during critical situations like real-world emergencies. In this study, we aim to explore the textual and temporal features present in social media posts (specifically tweets) related to COVID-19 to detect rumors as time is unique feature of text and any event can be mapped on timeline. Previous studies utilized the textual features and the temporal features are neglected at large for rumors detection. We utilize both temporal and textual features independently, as well as in combination, to train machine learning and neural network models. The evaluation of multiple algorithms (RNN, LSTM, CNN, DNN, BERT) across various feature sets reveals diverse performance. RNN and LSTM improve with combined textual and temporal features, highlighting temporal information's importance. CNN performs well with textual features but declines with temporal features. DNN maintains consistent performance, while BERT demonstrates moderate effectiveness in classification tasks. Time-aware features Textual features Rumor Detection Neural Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2025 Read the published version in Social Network Analysis and Mining → Version 1 posted Editorial decision: Revision requested 16 Nov, 2024 Reviews received at journal 03 Sep, 2024 Reviewers agreed at journal 24 Aug, 2024 Reviewers invited by journal 19 Aug, 2024 Editor assigned by journal 18 Aug, 2024 Submission checks completed at journal 12 Aug, 2024 First submitted to journal 10 Aug, 2024 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. 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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-4893251","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348948063,"identity":"d0182cc0-6970-42a9-b579-2ea479166341","order_by":0,"name":"Omel Mairaj","email":"","orcid":"","institution":"University of Sialkot","correspondingAuthor":false,"prefix":"","firstName":"Omel","middleName":"","lastName":"Mairaj","suffix":""},{"id":348948065,"identity":"77f274a0-5477-48a4-94b6-848719f17712","order_by":1,"name":"Shafiq Ur Rehman Khan","email":"data:image/png;base64,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","orcid":"","institution":"Capital University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Shafiq","middleName":"Ur Rehman","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2024-08-10 23:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4893251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4893251/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13278-025-01421-2","type":"published","date":"2025-03-13T15:58:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78689960,"identity":"ce382988-ce0e-4143-8eb4-74043d23c548","added_by":"auto","created_at":"2025-03-17 16:13:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":589352,"visible":true,"origin":"","legend":"","description":"","filename":"Submission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4893251/v1_covered_88e3abb5-13b0-4e31-aa52-a51b89a9a81c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling Temporal Patterns in Information for Improved Rumor Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"social-network-analysis-and-mining","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"snam","sideBox":"Learn more about [Social Network Analysis and Mining](http://link.springer.com/journal/13278)","snPcode":"13278","submissionUrl":"https://submission.nature.com/new-submission/13278/3","title":"Social Network Analysis and Mining","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Time-aware features, Textual features, Rumor Detection, Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-4893251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4893251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRumor detection is a critical task for addressing the spread of misinformation and maintaining the credibility of information sources. 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