Detecting Fake News: A Natural Language Processing, Reinforcement Learning, and Blockchain Approach

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Detecting Fake News: A Natural Language Processing, Reinforcement Learning, and Blockchain Approach</title> <meta name="description" content="Detecting Fake News: A Natural Language Processing, Reinforcement Learning, and Blockchain Approach"> <meta name="keywords" content="Natural Language Processing, Reinforcement Learning, Blockchain, fake news mitigation, misinformation detection."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Detecting Fake News: A Natural Language Processing, Reinforcement Learning, and Blockchain Approach"> <meta name="citation_author" content="Ashly Joseph"> <meta name="citation_author" content="Jithu Paulose"> <meta name="citation_publication_date" content="2024/08/28"> <meta name="citation_journal_title" content="International Journal of Computer and Information Engineering"> <meta name="citation_volume" content="18"> <meta name="citation_issue" 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class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value=""> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 33093</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Detecting Fake News: A Natural Language Processing, Reinforcement Learning, and Blockchain Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ashly%20Joseph">Ashly Joseph</a>, <a href="https://publications.waset.org/search?q=Jithu%20Paulose"> Jithu Paulose</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In an era where misleading information may quickly circulate on digital news channels, it is crucial to have efficient and trustworthy methods to detect and reduce the impact of misinformation. This research proposes an innovative framework that combines Natural Language Processing (NLP), Reinforcement Learning (RL), and Blockchain technologies to precisely detect and minimize the spread of false information in news articles on social media. The framework starts by gathering a variety of news items from different social media sites and performing preprocessing on the data to ensure its quality and uniformity. NLP methods are utilized to extract complete linguistic and semantic characteristics, effectively capturing the subtleties and contextual aspects of the language used. These features are utilized as input for a RL model. This model acquires the most effective tactics for detecting and mitigating the impact of false material by modeling the intricate dynamics of user engagements and incentives on social media platforms. The integration of blockchain technology establishes a decentralized and transparent method for storing and verifying the accuracy of information. The Blockchain component guarantees the unchangeability and safety of verified news records, while encouraging user engagement for detecting and fighting false information through an incentive system based on tokens. The suggested framework seeks to provide a thorough and resilient solution to the problems presented by misinformation in social media articles.</p> <iframe src="https://publications.waset.org/10013792.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Natural%20Language%20Processing" title="Natural Language Processing">Natural Language Processing</a>, <a href="https://publications.waset.org/search?q=Reinforcement%20Learning" title=" Reinforcement Learning"> Reinforcement Learning</a>, <a href="https://publications.waset.org/search?q=Blockchain" title=" Blockchain"> Blockchain</a>, <a href="https://publications.waset.org/search?q=fake%20news%20mitigation" title=" fake news mitigation"> fake news mitigation</a>, <a href="https://publications.waset.org/search?q=misinformation%20detection." title=" misinformation detection."> misinformation detection.</a> </p> <a href="https://publications.waset.org/10013792/detecting-fake-news-a-natural-language-processing-reinforcement-learning-and-blockchain-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013792/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013792/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013792/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013792/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013792/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013792/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013792/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013792/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013792/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013792/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013792.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">87</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Wang, Y., Yang, W., Ma, F., Xu, J., Zhong, B., et al. 2020. Weak Supervision for Fake News Detection via Reinforcement Learning. Proceedings of the AI Conference on Artificial Intelligence, 34(01): 516–523. <br>[2] Conroy, N. K., Rubin, V. L., & Chen, Y. 2015. Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52(1): 1–4. <br>[3] Yi-Chin Chen, Zhao-Yang Liu, and Hung-Yu Kao. 2017. IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 465–469, Vancouver, Canada. Association for Computational Linguistics. <br>[4] Popat, K., Mukherjee, S., Yates, A., & Weikum, G. 2018. DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning. arXiv https://doi.org/10.48550/arxiv.1809.06416. <br>[5] Xing, J., Wang, S., Zhang, X., & Ding, Y. 2021. HMBI: A New Hybrid Deep Model Based on Behavior Information for Fake News Detection. Wireless Communications and Mobile Computing, 2021: 1–7. <br>[6] Tschiatschek, S., Singla, A., Rodriguez, M. G., Merchant, A., & Krause, A. 2018. Fake News Detection in Social Networks via Crowd Signals. https://doi.org/10.1145/3184558.3188722. <br>[7] Elhadad, Mohamed K. et al. “Fake News Detection on Social Media: A Systematic Survey.” 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) (2019): 1-8. <br>[8] Van Der Walt, E., & Eloff, J. 2018. Using Machine Learning to Detect Fake Identities: Bots vs Humans. IEEE Access, 6: 6540–6549. <br>[9] Alkhodair, S. A., Ding, S. H. H., Fung, B. C. M., & Liu, J. 2020. Detecting breaking news rumors of emerging topics in social media. Information Processing & Management, 57(2): 102018. <br>[10] Ni, S., Li, J., & Kao, H.-Y. 2021. MVAN: Multi-View Attention Networks for Fake News Detection on Social Media. IEEE Access, 9: 106907–106917. <br>[11] Amer E, Kwak K-S, El-Sappagh S. Context-Based Fake News Detection Model Relying on Deep Learning Models. Electronics. 2022; 11(8):1255. https://doi.org/10.3390/electronics11081255 <br>[12] Girgis, S., Amer, E., & Gadallah, M.E. (2018). Deep Learning Algorithms for Detecting Fake News in Online Text. 2018 13th International Conference on Computer Engineering and Systems (ICCES), 93-97. <br>[13] Zhou, X., & Zafarani, R. 2019b. Network-based Fake News Detection: A Pattern-driven Approach. arXiv. https://doi.org/10.48550/arxiv.1906.04210 <br>[14] Shao C, Ciampaglia GL, Varol O, Yang KC, Flammini A, Menczer F. The spread of low-credibility content by social bots. Nat Commun. 2018 Nov 20;9(1):4787. doi: 10.1038/s41467-018-06930-7. PMID: 30459415; PMCID: PMC6246561. <br>[15] Sadiku M.N.O., Zhou Y., Musa S.M. Natural Language Processing. Int. J. Adv. Sci. Res. 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Detecting Fake News: A Natural Language Processing, Reinforcement Learning, and Blockchain Approach