A Hybrid Learning-Based Approach with Metadata Analysis to Credibility Classification of Web Domains

A Hybrid Learning-Based Approach with Metadata Analysis to Credibility Classification of Web Domains | 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 A Hybrid Learning-Based Approach with Metadata Analysis to Credibility Classification of Web Domains Son The Tran, Bao D. Nguyen, Dung Q. T. Truong, Sang A. Phung, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8545791/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The rapid growth of domain registrations and Internet use has led to an increase in abusive activities such as gambling, fraudulence, phishing, and malware. This circumstance occurs in most countries where the Internet is used as a common platform for communications. For example, in Vietnam, where there are over 600,000 registered domains, these abusive activities have severely caused financial and reputational damages and are extremely difficult to monitor. The existing manual approach to monitoring and identifying abusive domains is inefficient and labor-intensive. In this paper, we propose hybrid learning models that take advantage of domain lexical and metadata analysis to evaluate the credibility of registered domains and associated websites. Specifically, our hybrid learning models are designed as a fusion of joint features of the lexical domain encoder and RoBERTa-based encoders. To enhance the robustness of the models in practice, we applied a three-stage pipeline with a web-based interface. Throughout an extensive evaluation with realistic datasets, the proposed models demonstrate a substantial improvement over conventional baselines, offering an efficient and scalable solution to monitor abusive web domains in general. Web Credibility Classification Hybrid Learning Domain Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Feb, 2026 Editor assigned by journal 27 Feb, 2026 Submission checks completed at journal 08 Jan, 2026 First submitted to journal 07 Jan, 2026 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-8545791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598222871,"identity":"7b856ccf-83d9-468b-9d69-2d1f73a9c798","order_by":0,"name":"Son The Tran","email":"","orcid":"","institution":"University of Danang, Vietnam-Korea University of Information and Communication Technology","correspondingAuthor":false,"prefix":"","firstName":"Son","middleName":"The","lastName":"Tran","suffix":""},{"id":598222876,"identity":"ce902411-a506-494e-9b97-85037410e142","order_by":1,"name":"Bao D. 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