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

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

Rifshah Khan, Sandeep Kr. Mishra, Ritu Kumari, Sanjana Sinha, Shiwangi, WamikaGossain

Department of Computer Science, Babu Banaras Das Institute of Technology and Management, Lucknow, India

ABSTRACT
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.