Fake News Detection on Social Media using Machine Learning Techniques
Keywords:
Machine Learning, Support Vector MachineAbstract
Social media has been affecting life style of human being since last two decades by means of providing technologies, high end equipment and free environment for everyone to share their thoughts and ideas and also news. It also provide easy ways to create and propagate news rapidly across the globe. There are possibilities of spreading false news which might misguide the community, thesenews are called as Fake News. With the exponential growth in social media users, framing a central control is quite complex and challenging. Recongnizinga Fake News in Social Media is now a big issue for researchers in this field. Quite a few attempts are found in the literature for systematic recognization of such fake news. Fake news in social media has exclusive features which cannot be found in traditional methods and approaches for reality detection. This work explores the suitability of few popular machine learning techniques such as Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), Linear Regression (LR), Random Forest (RF) and Decision Tree (DT) for detection of fake news in social media data.
How to cite this article:
Kar BK, Kavitha L, Nayak SC. Fake News Detection on Social Media using Machine Learning Techniques. J Engr Desg Anal 2020; 3(2): 29-32.
References
Gilda S. Evaluating Machine Learning Algorithms for Fake News Detection. IEEE 15th Student Conference on Research and Development (SCOReD). 2017.
Shu K et al. Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newslett 2017b; 2017b; 19(1): 22-36.
Krishnan S, Chen M. Identifying Tweets with Fake News IEEE International Conference on Information Reuse and Integration for Data Science. 2018.
Shu K, Mahudeswaran D, Liu H. FakeNewsTracker: a tool for fake news collection, detection and visualization, Computational and Mathematical Organization Theory. Springer 2019; 25(1): 60-71.
Shu K, Sliva A, Wang S et al. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter 2017; 19(1): 22-36.
Aswani R, Kumar KA. Vigneswar IP. Exploring content virality in Facebook: A semantic based approach. Forthcoming in Lecture Notes in Computer Science. In Proceedings of 16th IFIP Conference on e-Business, e-Services and e-Society. Springer International Publishing., in press. 2017d.
Buzzfeednews: 2017-12-fake-news-top-50. https://github. com/BuzzFeedNews/2017-12-fake-newstop-50.
Maheshwari S. How fake news goes viral: A case study. 2016. [Online]. Available: https://www.nytimes.com/2016/11/20/business/media/how-fake-news-spreads.html.
Gupta A, Kaushal. Improving Spam Detection in Online Social Networks. 2015; 978-1-4799-71718/15/$31.00©2015 IEEE.
Shu K, Wang S, Liu H. Understanding user profiles on social media for fake news detection. In: IEEE conference on multimedia information processing and retrieval (MIPR). 2018b; 430-435.
Wang WY. liar, liar pants on fire: a new benchmark dataset for fake news detection. 2017. arXiv:1705.00648.
Ruchansky N, Seo S, Liu Y. CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management. ACM. 2017.
Hu X, Tang J, Gao H et al. Social spammer detection with sentiment information. In ICDM’14.
Wang S, Tang J, Aggarwal C et al. Linked document embedding for classi.cation. In CIKM’16.
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