Fake News Detection on Social Media

Authors

  • Prof. Dipti Chaudhari  Computer Engineering, D. Y. Patil Institute of Technology, Pune, Maharashtra, India
  • Krina Rana  Computer Engineering, D. Y. Patil Institute of Technology, Pune, Maharashtra, India
  • Radhika Tannu  Computer Engineering, D. Y. Patil Institute of Technology, Pune, Maharashtra, India
  • Snehal Yadav  Computer Engineering, D. Y. Patil Institute of Technology, Pune, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT2063219

Keywords:

Machine Learning, Support Vector Machine, Tweeter, Online news.

Abstract

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and the articles which are circulated among the social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs and social networking sites. It can be considered that social media has replaced the traditional media and become one of the main platforms for spreading news. News on social media trends to travel faster and easier than traditional news sources due to the internet accessibility and convenience. It is harmful for the society to believe on the rumors and pretend to be a news. The basic need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and methodology for fake news detection. With the help of Machine Learning, we tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. Even we have presented the mechanism to identify the significant Tweet's attribute and application architecture to systematically automate the classification of the online news.

References

  1. Gottfried, J. & Shearer, E. (2016, May 26). ”News use across social media platforms 2016.” Retrieved from http://www.journalism.org/2016/05/26/news-use-across-social-mediaplatforms-2016/
  2. Allcott, H. and Gentzkow, M. (2018). Social Media and Fake News in the 2016 Election. [online] NBER. Available at: http://www.nber.org/papers/w23089.
  3. Silverman, C. (2016, November 17). ”This Analysis Shows How Viral Fake Election News Stories Outperformed Real News On Facebook.” Retrieved from https://www.buzzfeed.com/craigsilverman/viralfake-election-news-outperformed-real-news-on-facebook
  4. Shu, K., Sliva, A., Wang, S., Tang, J. and Liu, H. (2018). Fake News Detection on Social Media: A Data Mining Perspective. [online]
  5. Arxiv.org. Available at: https://arxiv.org/abs/1708.01967 [Accessed 16 Mar. 2018].
  6. Bajaj, S. ”Fake News Detection Using Deep Learning.” Available at: https://web.stanford.edu/class/cs224n/reports/2710385.pdf
  7. Singh, V., Dasgupta, R., Sonagra, D., Raman, K. & Ghosh I. Automated Fake News Detection Using Linguistic Analysis and Machine Learning.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2020-06-30

Issue

Volume 6, Issue 3, May-June-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. Dipti Chaudhari, Krina Rana, Radhika Tannu, Snehal Yadav, " Fake News Detection on Social Media" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.1132-1137, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT2063219