Fake News Detection: A Comprehensive Methodology Utilizing Topic Modeling and Machine Learning

In this work, we present a thorough methodology that makes use of advanced machine learning algorithms and natural language processing tools to identify fake news. We used Latent Dirichlet Allocation (LDA) for topic modeling in order to identify latent topics within the articles. To depict the topics in a lower-dimensional space, dimensionality reduction techniques like T-Distributed Stochastic Neighbor embedding (t-SNE) are used. To discern between authentic and fake news stories, classification algorithms undergo training using topic representations. We assess the effectiveness of proposed method and obtain 95.64% accuracy, 1.0293 AUC, 0.9347 recall, 0.9595 precision, and 0.9466 F1-score. These outcomes demonstrate how well our methodology works to address this important problem.