Research paper on Detection of fake news using Naive Bayes and Passive Agressive Classifier

The widespread presence of false information in the form of fake news poses a significant problem for society today. It is crucial to develop effective techniques to automatically identify and combat this issue. This research paper presents a comprehensive method for detecting fake news that combines the Naive Bayes theorem, TF-IDF vectorizer, and an aggressive passive classifier. By leveraging the Naive Bayes algorithm, our approach utilizes a probabilistic framework to analyze the data. The TF-IDF vectorizer helps in capturing the importance of words within documents, aiding in the detection process. Additionally, the aggressive passive classifier enhances the accuracy of classification by incorporating a self-training mechanism. To validate our proposed approach, we conducted experiments on a benchmark dataset. The results demonstrated promising performance, surpassing existing methods in terms of accuracy, precision, recall, and F1-score. This research contributes to the advancement of fake news detection methods and offers a valuable solution to the challenges posed by the proliferation of false information in modern society.