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@article{190477,
        author = {Jitendra Malviya and Nitya Khare},
        title = {A Comparative Analysis of Multimodal BERT Architectures for Fake News Detection on the LIAR Dataset},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {3897-3900},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190477},
        abstract = {The accelerating spread of misinformation across digital media has created an urgent demand for robust automatic fake news detection systems. Yet most existing approaches focus narrowly on textual content, neglecting rich contextual metadata that can help disambiguate subtle, politically charged statements. This study presents a detailed comparative analysis of advanced multimodal BERT architectures for fake news detection, specifically on the challenging LIAR dataset. Building upon baseline research that achieved 59.56% accuracy with a lightweight, text-only transformer—we examine whether integrating contextual features with state-of-the-art BERT variants improves detection performance. Our experiments systematically can evaluate DeBERTa-v3 and RoBERTa-large architectures enhanced with speaker profiles, political affiliations, and historical credibility signals we are expecting that multimodal integration yields measurable gains, with DeBERTa-v3 reaching more accuracy, surpassing current state-of-the-art approaches. These findings underscore the importance of contextual signals in misinformation detection and provide actionable guidance for architecture selection in politically sensitive NLP tasks.},
        keywords = {Fake News Detection; Multimodal BERT; LIAR Dataset; Transformer Architectures; Metadata Integration},
        month = {January},
        }