FAKE DETECH ADEEP LEARNING ENSEMBLE MODEL FOR SFAKE NEWS DETECTION

Abstract

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset. In the era of information explosion, the spread of fake news poses a significant threat to public discourse and trust in the media. This project aims to develop a robust fake news detection system using a deep learning ensemble model. The ensemble model will leverage the strengths of multiple deep learning architectures to enhance the accuracy and reliability of fake news identification.