A Review on Fake News Detection

Abstract

Abstract: The widespread increase of fake news, generated by both humans and machines, has negative impacts on both society and individuals, politically and socially. The fast-paced nature of social networks makes it difficult to promptly evaluate the reliability of news. Hence, there is a growing need for automated tools to detect fake news. Compared to traditional machine learning techniques, deep learning-based approaches have shown higher accuracy in detecting fake news. Attention and Bidirectional Encoder Representations for Transformers are some of the emerging deep learning-based methods used for this task. A hybrid Neural Network architecture, which combines CNN and LSTM, is also used along with two different dimensionality reduction techniques, PCA and Chi-Square. These techniques are compared with regular ML techniques such as Decision Tree, logistic Regression, K Nearest Neighbor, Random Forest, Support Vector Machine, and Naive Bayes, as well as RNN and LSTM, in terms of parameters like F1-score and accuracy. The goal is to identify the best approach for detecting fake news.