Real-Time Profiling and Mitigation of Irony and Stereotype Spreaders on Twitter Using a Domain-Adaptive NLP Pipeline

Real-Time Profiling and Mitigation of Irony and Stereotype Spreaders on Twitter Using a Domain-Adaptive NLP Pipeline | 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 Short Report Real-Time Profiling and Mitigation of Irony and Stereotype Spreaders on Twitter Using a Domain-Adaptive NLP Pipeline Sravan Kumar, Suresh Kumar Mandala, Dr. Satyanarayana Vollala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7531921/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 Real-time detection and mitigation of irony and stereotype spreaders on Twit-ter is vital for content moderation. We propose a domain-adaptive NLP pipeline combining transformer models, graph neural networks, and reinforcement learning to profile and prioritize such users. Using domain-adaptive embeddings and user interaction analysis, our approach excels across domains like politics, entertainment , and sports. Evaluated on the TwiBot-22 dataset, augmented with additional irony and stereotype annotations from over 200 million tweets, it achieves a 5–8% F1-score increase (reaching 85–88%) and 4–7% accuracy boost (reaching 79–82%) over state-of-the-art baselines, offering a scalable solution for Twitter’s content ecosystem. Irony Detection Stereotype Detection Domain Adaptation Graph Neural Networks Reinforcement Learning Full Text Additional Declarations No competing interests reported. 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. 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-7531921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":531778080,"identity":"feec9dc8-c615-4539-9204-b268cf9b6443","order_by":0,"name":"Sravan 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