{"paper_id":"48db7ecf-d0f1-4fa7-a5ae-d209ffcaea98","body_text":"AI-Powered Fake News Detection Tool for Nepali 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PDF Research Article AI-Powered Fake News Detection Tool for Nepali Media Ghimire Plan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6778598/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 The rapid proliferation of misinformation poses significant challenges to public discourse in Nepal, particularly given the prevalence of bilingual content and varied literacy levels. This study introduces a robust, bilingual (Nepali/English) fake-news detection framework that integrates a web application and a browser extension to facilitate on-demand credibility assessment. The system’s backend is implemented in Django, while the frontend leverages React with Vite for efficient rendering. Incoming articles are ingested via URL scraping (Beautiful Soup) or direct text input, then subjected to Unicode-based filtering, custom Nepali stemming, and sequence tokenization. Feature extraction combines TF-IDF vectors (10,000-token vocabulary) and dense embeddings (100-dimensional), which feed an ensemble of five classifiers: a bidirectional LSTM (2 layers, hidden size 256, dropout 0.5), logistic regression, gradient boosting, random forest, and an isolation-forest one-class detector. Model outputs are aggregated by weighted averaging—weights optimized to maximize validation ROC-AUC. Evaluated on a balanced Nepali-English news corpus, the ensemble achieved 91.2 % accuracy, 0.93 precision, 0.92 recall, and 0.96 ROC-AUC, outperforming single-model baselines. User studies (n = 15) confirmed high usability and effectiveness in flagging fabricated content. These results demonstrate the tool’s potential to enhance digital literacy and mitigate the spread of fake news across Nepal’s media ecosystem. Artificial Intelligence and Machine Learning Fake News Detection Nepali Media Bilingual NLP Machine Learning Digital Literacy Browser Extension Misinformation Full Text Additional Declarations The authors declare no competing interests. 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. 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