Detecting Fake Information Dissemination using Leveraging Machine Learning and DRIMUX with B-LSTM - Kaneti, Venkata Ramana; Venkatesan, Mithra; Sharma, Himanshu; Bodhankar, Aniruddha; Bajaj, Ram; Kumar R., Ganesh; Pokhariya, Hemant Singh; Deepak, A. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.