Transfer Learning Driven Fake News Detection and Classification using Large Language Models

Transfer Learning Driven Fake News Detection and Classification using Large Language Models | 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 Transfer Learning Driven Fake News Detection and Classification using Large Language Models Basma S. Alqadi, Suliman A. Alsuhibany, Samia Nawaz Yousafzai, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6457779/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Today, the problem of using social media to spread false information is not only widespread but also quite serious. The extensive dissemination of fake news, regardless of whether it is produced by human beings or computer programs, has a negative impact not only on society but also on individuals in terms of politics and society. Currently of social networks, the quick dissemination of news provides a challenge when it comes to establishing the reliability of the information in a satisfactory manner. Because of this, the requirement for automated technologies that can identify fake news has become of the utmost importance. In this work, the application of transfer learning with several different large language models was utilized, which resulted in an improvement in the efficiency of decision-making about bogus news. In addition, we study several different words embedding techniques, such as Word2Vec and one-hot encoding, and we introduce the capacity of transfer learning to deep learning and machine learning algorithms for the aim of comparing their respective levels of performance. Based on the findings of our experiments conducted on two datasets derived from the real world, we have determined that the transfer learning-based strategy that we have developed can outperform the most advanced approaches by a minimum of 3.9% in terms of accuracy and offering a rational explanation. Health sciences/Risk factors Health sciences/Signs and symptoms Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 May, 2025 Reviews received at journal 21 May, 2025 Reviews received at journal 15 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviews received at journal 14 May, 2025 Reviews received at journal 08 May, 2025 Reviewers agreed at journal 01 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Editor invited by journal 24 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 15 Apr, 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. 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-6457779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":450569923,"identity":"cb77b07b-c654-4c3c-a588-e3a0ef110911","order_by":0,"name":"Basma S. 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