Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{152424,
        author = {Prince jain and Anubhav Tiwari and Tushar Gupta and Abhishek and Vineet Srivastava},
        title = {FAKE NEWS DETECTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {3},
        pages = {385-388},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152424},
        abstract = {The automation of fake news detection is the focus of a great deal of scientific research. With the rise of social media over the years, there has been a strong preferences for users to be informed using their social media account, leading to a proliferation of fake news through them. Nowadays fake news spread very fast . The credibility of social media networks is also at stake where the spreading of fake information is prevalent. Thus fake news detection has become a challenging topic nowadays. In this work we use the dataset which is collected from the kaggle.com for fake news detection and it is publicly available for use, which provides links to source documents for each case. As such, the goal of this project was to create  a tool for detecting the language pattern that characterize fake and real news through the use of Machine Learning techniques. The result of this project demonstrate the ability for machine learning to be useful in this task. We have built built a model that catches many intuitive indications of real and fake news.},
        keywords = {Deep learning (DL), Machine Learning (ML), Naive Bayes Models, Classification.},
        month = {},
        }