{"title":"Detecting Fake News: A Natural Language Processing, Reinforcement Learning, and Blockchain Approach","authors":"Ashly Joseph, Jithu Paulose","volume":212,"journal":"International Journal of Computer and Information Engineering","pagesStart":570,"pagesEnd":576,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10013792","abstract":"<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>","references":"[1]\tWang, 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\u2013523.\r\n[2]\tConroy, 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\u20134.\r\n[3]\tYi-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\u2013469, Vancouver, Canada. Association for Computational Linguistics.\r\n[4]\tPopat, K., Mukherjee, S., Yates, A., & Weikum, G. 2018. 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