Detection of Fake News Using Machine Learning



news, fake news detection, machine learning, passive aggressive classifier


For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news.


Download data is not yet available.


Michele Banko, Michael J Cafarella, Stephen Soder-land, Matthew Broadhead, and Oren Etzioni. Open information extraction from the web. InIJCAI'07.

Amr Magdy and NayerWanas. Web-based statistical fact checking of textual documents. In Proceedings of the 2nd international workshop on Search and mining user-generated contents, pages 103{110. ACM,2010.

Giovanni Luca Ciampaglia, Prashant Shiralkar,Luis M Rocha, Johan Bollen, Filippo Menczer, andAlessandroFlammini. Computational fact checking from knowledge networks. PloS one,10(6):e0128193,2015.

Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, Huan Liu. "Fake News Detection on Social Media", ACM SIGKDD Explorations Newsletter,2017

Local tampering detection in video sequences Paolo Bestagini, Simone Milani, Marco Tagliasacchi, StefanoTubaro

Andrew Ward, L Ross, E Reed, E Turiel, and T Brown. Naive realism in everyday life: Implications for social conict and misunderstanding. Values and knowledge, pages 103{135,1997.

Wikipedia tf-idf

Passive aggressive algorithm

“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection, William Yang Wang




How to Cite

M. S. Rawat, A. . Srivastava, and S. . Aggarwal, “Detection of Fake News Using Machine Learning”, International Journal of Engineering and Applied Physics, vol. 1, no. 2, pp. 205–209, May 2021.