Detecting Fake Information Dissemination using Leveraging Machine Learning and DRIMUX with B-LSTM
Keywords:
Fake Information Dissemination Detection, Machine Learning, DRIMUX, Accuracy, B-LSTMAbstract
Information integrity and public confidence are seriously threatened by the rapid expansion of fake news and misinformation that has resulted from the online broadcast of information. This work focuses on the detection of fraudulent information propagation utilizing machine learning techniques and the Digital Reputation and Influence Measurement Unit (DRIMUX) in order to address this problem. The use of Bidirectional Long Short-Term Memory (B-LSTM) networks into the detection process is something we really advocate. B-LSTM enables the capture of contextual dependencies from both past and future time steps, enhancing the understanding of sequential data. Additionally, DRIMUX provides reputation and influence measurements to assess the credibility of information sources. Experimental analyses on various datasets reveal the promising performance of the suggested methodology, highlighting its potential in preventing the spread of false information and protecting the veracity of digital information.
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Pulido, C. M., Villarejo-Carballido, B., Redondo-Sama, G., & Gómez, A. (2020). COVID-19 Infodemic: More Retractions of Scientific Papers as Research Misconduct. Journal of Medical Internet Research, 22(5), e19411.
Zhang, H., Zhang, C., Wang, X., Li, J., & Liu, Y. (2020). Fake News Detection with Deep Learning Models and Data Augmentation. IEEE Access, 8, 215060-215068.
Zhang, X., Zhao, J., & LeCun, Y. (2015). Character-level Convolutional Networks for Text Classification. Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS), 649-657.
Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532-1543.
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
Ma, J., Gao, W., Wei, X., Lu, J., & Wong, K. F. (2019). DRIMUX: Detect Rumors Using Influence Maximization. IEEE Transactions on Knowledge and Data Engineering, 31(10), 1940-1953
Castillo, C., Mendoza, M., & Poblete, B. (2011). Information Credibility on Twitter. Proceedings of the 20th International Conference on World Wide Web (WWW), 675-684
Ma, J., Gao, W., Wei, X., Lu, J., & Wong, K. F. (2018). Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 17A. Douglas, “News consumption and the new electronic media,” The International Journal of Press/Politics, vol. 11, no. 1, pp. 29–52, 2006.
J. Wong, “Almost all the traffic to fake news sites is from facebook, new data show,” 2016.
D. M. J. Lazer, M. A. Baum, Y. Benkler et al., “The science of fake news,” Science, vol. 359, no. 6380, pp. 1094–1096, 2018.
S. A. García, G. G. García, M. S. Prieto, A. J. M. Guerrero, and C. R. Jiménez, “The impact of term fake news on the scientific community scientific performance and mapping in web of science,” Social Sciences, vol. 9, no. 5, 2020.
A. D. Holan, 2016 Lie of the Year: Fake News, Politifact, Washington, DC, USA, 2016.
S. Kogan, T. J. Moskowitz, and M. Niessner, “Fake News: Evidence from Financial Markets,” 2019, https://ssrn.com/abstract=3237763.
A. Robb, “Anatomy of a fake news scandal,” Rolling Stone, vol. 1301, pp. 28–33, 2017.
J. Soll, “The long and brutal history of fake news,” Politico Magazine, vol. 18, no. 12,
J. Hua and R. Shaw, “Corona virus (covid-19) “infodemic” and emerging issues through a data lens: the case of China,” International Journal of Environmental Research and Public Health, vol. 17, no. 7, p. 2309, 2020.
Neha Sharma, P. William, Kushagra Kulshreshtha, Gunjan Sharma, Bhadrappa Haralayya, Yogesh Chauhan, Anurag Shrivastava, “Human Resource Management Model with ICT Architecture: Solution of Management & Understanding of Psychology of Human Resources and Corporate Social Responsibility”, JRTDD, vol. 6, no. 9s(2), pp. 219–230, Aug. 2023.
William, P., Shrivastava, A., Chauhan, P.S., Raja, M., Ojha, S.B., Kumar, K. (2023). Natural Language Processing Implementation for Sentiment Analysis on Tweets. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_26
K. Maheswari, P. William, Gunjan Sharma, Firas Tayseer Mohammad Ayasrah, Ahmad Y. A. Bani Ahmad, Gowtham Ramkumar, Anurag Shrivastava, “Enterprise Human Resource Management Model by Artificial Intelligence to Get Befitted in Psychology of Consumers Towards Digital Technology”, JRTDD, vol. 6, no. 10s(2), pp. 209–220, Sep. 2023.
Kumar, A., More, C., Shinde, N. K., Muralidhar, N. V., Shrivastava, A., Reddy, C. V. K., & William, P. (2023). Distributed Electromagnetic Radiation Based Sree Lakshmi, P., Deepak, A., Muthuvel, S.K., Amarnatha Sarma, C Design and Analysis of Stepped Impedance Feed Elliptical PatchAntenna Smart Innovation, Systems and Technologies, 2023, 334, pp. 63
Gupta, A., Mazumdar, B.D., Mishra, M., ...Srivastava, S., Deepak, A., Role of cloud computing in management and education, Materials Today: Renewable Energy Assessment Using Novel Ensembling Approach. Journal of Nano-and Electronic Physics, 15(4)
N. K. Conroy, V. L. Rubin, and Y. Chen, “Automatic deception detection: methods for finding fake news,” Proceedings of the Association for Information Science and Technology, vol. 52, no. 1, pp. 1–4, 2015.
Wang, W. Y. (2017). "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Volume 2, 422-426.
Shu, K., Mahudeswaran, D., Wang, S., Lee, D. L., & Liu, H. (2017). Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, 19(1),
Mondal, D., & Patil, S. S. (2022). EEG Signal Classification with Machine Learning model using PCA feature selection with Modified Hilbert transformation for Brain-Computer Interface Application. Machine Learning Applications in Engineering Education and Management, 2(1), 11–19. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/20
Mr. Rahul Sharma. (2013). Modified Golomb-Rice Algorithm for Color Image Compression. International Journal of New Practices in Management and Engineering, 2(01), 17 - 21. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/13
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