Fake news Detection on online Social Media

Fake news Detection on online Social Media | 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 Fake news Detection on online Social Media Ashwini Deshmukh, Dr. Sharvari Govilkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5254328/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract In today's digital age, social media platforms are key for information sharing, but they also facilitate the rapid spread of fake news, especially during events like the COVID-19 pandemic. A system has been developed to address this issue by categorizing news articles into six categories, from "true" to "pants on fire." Data is gathered from diverse sources like Facebook, Twitter, YouTube, and trusted organizations such as WHO and UNICEF. Techniques like Principal Component Analysis (PCA) and machine learning algorithms, including Bi-LSTM neural networks with attention mechanisms, help improve detection accuracy. Despite challenges with multi-class datasets, the system achieved 51% accuracy and a 44.9% F-score. The system also assesses the credibility of news sources and authors by evaluating social media activity and potential biases. Further improvements are sought to refine performance and expand across social media platforms. Bidirectional Long Short-Term Memory (Bi-LSTM) Deep learning Fake news detection Machine learning Natural language processing (NLP) Principal Component Analysis (PCA) Social media Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviews received at journal 13 Mar, 2025 Reviewers agreed at journal 28 Feb, 2025 Reviewers agreed at journal 28 Feb, 2025 Reviewers invited by journal 28 Feb, 2025 Editor assigned by journal 14 Dec, 2024 Submission checks completed at journal 09 Nov, 2024 First submitted to journal 13 Oct, 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. 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