Design of an Iterative Cross-Modal and Context-Aware Deep Analytical Framework for Hate Speech and Fake Post Detection on Social Media Sets

Design of an Iterative Cross-Modal and Context-Aware Deep Analytical Framework for Hate Speech and Fake Post Detection on Social Media Sets | 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 Design of an Iterative Cross-Modal and Context-Aware Deep Analytical Framework for Hate Speech and Fake Post Detection on Social Media Sets Rakesh Bharati, Jyoti Bharti, Vasudev Dehalwar, Jaydeep Kishore This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7487462/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 exponential growth of user-generated content on social media platforms has increased the diffusion of hate speech and fake information considerably, which poses serious risks to the integrity of societies and the public trust people vest in them. Existing approaches tend to be critically limited by core restrictions such as unimodal processing, absence of user-context integration, poor cross-modal synchronizations, and weak robustness against adversarial manipulations. Such restrictions lead compounded losses in generalizations, misclassifications, and unreliability of deployments in real-life scenarios. For these challenges, this work offers a complete, modular, and analytically validated pipeline around five newly developed methods for multimodal hate speech and fake post detection from a Twitter corpus. The first component is Context-Driven Social Vector Extraction, which produces enriched contextual embeddings by capturing text, image, temporal, and behavioural metadata. These embeddings are processed by the Cross-Modal Label Fusion via Mutual Co-Attention (CMF-MCA) module, which utilizes a dual-transformer with co-attention to jointly annotate textual and visual information. The third module brought into play is Semantic Propagation Graph for Hate & Fake Correlation (SPG-HFC), which implements a multi-relational graph attention mechanism that captures semantic influence and community-level propagation patterns. The next step is located in the Adaptive Modality Routing via Reinforcement (AMR-R) module which selects, depending on the input complexity, between machine learning and deep learning classifiers using a reinforcement policy network. Finally, the Counterfactual Consistency Validation Engine (CCVE) performs post-prediction verification by evaluating counterfactual variants to ensure robustness and semantic consistency. Thus, detection accuracy, scalability, and inference reliability significantly increase in this integrated framework. It transcends the realm by making permissible cross-modal, context-aware, and behaviour-driven classification, which in turn greatly supports the development of large-scale secure and trustworthy content moderation systems in process. Physical sciences/Engineering Physical sciences/Mathematics and computing Hate Speech Detection Fake News Classification Cross-Modal Learning Graph Attention Networks Social Media Analytics 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-7487462","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538648076,"identity":"fd18096f-3155-49c4-bac2-ac01658ff4f8","order_by":0,"name":"Rakesh Bharati","email":"","orcid":"","institution":"Maulana Azad National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Bharati","suffix":""},{"id":538648077,"identity":"0fa65dbf-ba11-404c-8d9b-74225c79e020","order_by":1,"name":"Jyoti Bharti","email":"","orcid":"","institution":"Maulana Azad National Institute of 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