{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:47:19Z","timestamp":1780588039299,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Biomedical Sensors and Systems Lab, University of Memphis, Memphis, TN 38152, USA"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>The widespread rise of misinformation across digital platforms has increased the demand for accurate and efficient Fake News Detection (FND) systems. This study introduces an enhanced transformer-based architecture for FND, developed through comprehensive ablation studies and empirical evaluations on multiple benchmark datasets. The proposed model combines improved multi-head attention, dynamic positional encoding, and a lightweight classification head to effectively capture nuanced linguistic patterns, while maintaining computational efficiency. To ensure robust training, techniques such as label smoothing, learning rate warm-up, and reproducibility protocols were incorporated. The model demonstrates strong generalization across three diverse datasets, such as FakeNewsNet, ISOT, and LIAR, achieving an average accuracy of 79.85%. Specifically, it attains 80% accuracy on FakeNewsNet, 100% on ISOT, and 59.56% on LIAR. With just 3.1 to 4.3 million parameters, the model achieves an 85% reduction in size compared to full-sized BERT architectures. These results highlight the model\u2019s effectiveness in balancing high accuracy with resource efficiency, making it suitable for real-world applications such as social media monitoring and automated fact-checking. Future work will explore multilingual extensions, cross-domain generalization, and integration with multimodal misinformation detection systems.<\/jats:p>","DOI":"10.3390\/jcp5030043","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:26:53Z","timestamp":1752229613000},"page":"43","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Towards Reliable Fake News Detection: Enhanced Attention-Based Transformer Model"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7597-131X","authenticated-orcid":false,"given":"Jayanti","family":"Rout","sequence":"first","affiliation":[{"name":"P. G. Department of Computer Science, Fakir Mohan University, Balasore 756019, India"},{"name":"Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6784-4449","authenticated-orcid":false,"given":"Minati","family":"Mishra","sequence":"additional","affiliation":[{"name":"P. G. Department of Computer Science, Fakir Mohan University, Balasore 756019, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-4333","authenticated-orcid":false,"given":"Manob Jyoti","family":"Saikia","sequence":"additional","affiliation":[{"name":"Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA"},{"name":"Electrical and Computer Engineering Department, University of Memphis, Memphis, TN 38152, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"ref_1","unstructured":"IANS (2025, June 20). Nearly Half of the Fake News Stories in India Are Political: Study. Available online: https:\/\/www.ndtv.com\/india-news\/nearly-half-of-the-fake-news-stories-in-india-are-political-study-7291481."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zafarani, R., Shu, K., and Liu, H. 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