A Novel Approach to Fake News Detection Using Generative AI
Keywords:
Fake news, Misinformation, Detection, Reliable information, Text analytics, Article summarization, User behaviour, Twitter analytics, Authentic sources, LlamaIndex, WhatsApp bot, Large Language Model (LLM)Abstract
Fake news has a significant impact on society, making the detection of such misinformation crucial. It undermines trust in reliable information sources, distorts public opinions, and can even influence political outcomes. Detecting fake news is important to ensure that users receive accurate and authentic information, maintain a trustworthy news ecosystem, and prevent the spread of misinformation. Directly classifying a fake news to be fake on some parameters is not possible. Here, the news article will be evaluated on the main three parameters, first is Text Analytics which includes identifying the exaggerated or propagandistic statements or the type of speech is been used in the article like acceptable, non acceptable, offensive, etc. and also through summarization, we get the context about the article, second is user behaviour through twitter analytics guides to understand the user reaction towards the article on the real time basis and at last, through indexing the authentic source in the index of Large Language Model build using LlamaIndex. This methodology integrated with the whatsapp bot showcased the better result to identify the fake news and ensure the user that the news is authentic or not.
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J. Jieun Shin, Lian Jian, Kevin Driscoll, François Bar, “The diffusion of misinformation on social media: Temporal pattern, message, and source, Computers in Human Behavior”, Volume 83, 2018, Pages 278-287, ISSN 0747-5632, https://doi.org/10.1016/j.chb.2018.02.008.
Xichen Zhang, Ali A. Ghorbani, “An overview of online fake news: Characterization, detection, and discussion, Information Processing & Management, Volume 57, Issue 2, 2020, 102025, ISSN 0306-4573, https://doi.org/10.1016/j.ipm.2019.03.004.
S. Singhal, R. R. Shah, T. Chakraborty, P. Kumaraguru and S. Satoh, "SpotFake: A Multi-modal Framework for Fake News Detection," 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), Singapore, 2019, pp. 39-47, doi: 10.1109/BigMM.2019.00-44.
Kai Shu, Suhang Wang, and Huan Liu, “Beyond News Contents: The Role of Social Context for Fake News Detection”, In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19), Association for Computing Machinery, New York, NY, USA, 2019, 312–320. https://doi.org/10.1145/3289600.3290994
Victoria L Rubin, Niall J Conroy, Yimin Chen, and Sarah Cornwell, “Fake news or truth? using satirical cues to detect potentially misleading news”, In Proceedings of NAACL-HLT, 2016, pages 7–17.
Vosoughi S, Roy D, Aral S, “The spread of true and false news online”, Science, 2018, 359(6380):1146-1151. doi:10.1126/science.aap9559
Gianmarco De Francisci Morales, Alessandro Lulli, Luca Pappalardo, "Automatic Detection of Fake News", 2017. arXiv:1708.07104
K. Węcel et al., “Artificial intelligence - friend or foe in fake news campaigns”, Economics and Business Review, 2023, vol. 9, Art. no. 2. doi: 10.18559/ebr.2023.2.736.
Sean, B., Doug, S., Yuxi, P., “Talos Targets Disinformation with Fake News Challenge Victory”, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin, “Attention is all you need”, In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17), 2017
Jwa, Heejung, Dongsuk Oh, Kinam Park, Jang Mook Kang, and Heuiseok Lim, "exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)" Applied Sciences 9, no. 19: 4062, 2019. https://doi.org/10.3390/app9194062
Szczepański, M., Pawlicki, M., Kozik, R. et al., “New explainability method for BERT-based model in fake news detection”, Sci Rep 11, 23705 (2021), https://doi.org/10.1038/s41598-021-03100-6
Shu K, Sliva A, Wang S, Tang J, Liu H, “Fake news detection on social media: a data mining perspective”, ACM SIGKDD Explor Newsl 19(1):22–36. https://doi.org/10.1145/3137597.3137600
Ruchansky N, Seo S, Liu Y, “Csi: a hybrid deep model for fake news detection”, In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 797–806, 2017. https://doi.org/10.1145/3132847.3132877
Zhang X, Ghorbani AA, “An overview of online fake news: characterization, detection, and discussion”, Inf Process Manag 57(2):102025, 2020. https://doi.org/10.1016/j.ipm.2019.03.004
Yazdi KM, Yazdi AM, Khodayi S, Hou J, Zhou W, Saedy S, “Improving fake news detection using k-means and support vector machine approaches”, Int J Electron Commun Eng 14(2):38–42, 2020. https://doi.org/10.5281/zenodo.3669287
Edell, A. (2018) “Trained Fake News Detection AI with >95% Accuracy, and Almost Went Crazy.” Towards Data Science
Sonal Garg, Dilip Kumar Sharma, “Linguistic features based framework for automatic fake news detection”, Computers & Industrial Engineering, Volume 172, Part A, 2022, 108432, ISSN 0360-8352. https://doi.org/10.1016/j.cie.2022.108432.
Zhuoran Lu, Patrick Li, Weilong Wang, and Ming Yin, “The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social Influence”, Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Article 461, November 2022, 27 pages. https://doi.org/10.1145/3555562
Mr. Rahul Sharma. (2015). Recognition of Anthracnose Injuries on Apple Surfaces using YOLOV 3-Dense. International Journal of New Practices in Management and Engineering, 4(02), 08 - 14. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/36
Jhade, S. ., Kumar, V. S. ., Kuntavai, T. ., Shekhar Pandey, P. ., Sundaram, A. ., & Parasa, G. . (2023). An Energy Efficient and Cost Reduction based Hybridization Scheme for Mobile Ad-hoc Networks (MANET) over the Internet of Things (IoT). International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 157–166. https://doi.org/10.17762/ijritcc.v11i2s.6038
Juneja, V., Singh, S., Jain, V., Pandey, K.K., Dhabliya, D., Gupta, A., Pandey, D. Optimization-based data science for an IoT service applicable in smart cities (2023) Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities, pp. 300-321.
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