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Techniques of Fake News Detection( Vol-6,Issue-2,March 2020 )

Author(s):

Harshit Garg, Ms. Alisha Goyal, Ms. Ankita Joshi

Keywords:

Fake News, Machine Learning, Classifiers.

Abstract:

Fake news is the contents that claim people to believe with the falsification, sometime it is the sensitive messages. Mixing both believable and unbelievable information on social media has made the confusion of truth. That is the truth will be hardly classified. The techniques for detecting the Fake News means its a false story which comes from unauthorized source. Only by building a model based on a count vectorizer or a Term Frequency- Inverse Document Frequency i.e. TF-IDF score matrix calculation can only get you so far. It may happen that the meaning two article be similar. Combating the fake news is a classic text classification project with a straight forward proposition. We can implement a task by Naïve Bayes or any other method to find out the real vs fake news.

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References:

[1] Nir Kshetri, Jeffrey Voas, “The Economics of Fake News”, IEEE, IT Professional, 2017, Volume: 19, Issue: 6, Pages: 8 - 12
[2] Roger Musson, “Views: The frost report: fake news is nothing new”, 2017, IEEE, Astronomy & Geophysics, Volume: 58, Issue: 3, Pages: 3.10 - 3.10
[3] Hal Berghel, “Oh, What a Tangled Web: Russian Hacking, Fake News, and the 2016 US Presidential Election”, IEEE, Computer, 2017, Volume: 50, Issue: 9, Pages: 87 - 91
[4] Hal Berghel, “Alt-News and Post-Truths in the "Fake News" Era”, IEEE, Computer, 2017, Volume: 50, Issue: 4.