A Review of Machine Learning-Based Fake News Analysis

Authors

  • Vaishali Singh  Assistant Professor, Department of Computer Science and Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India

DOI:

https://doi.org/10.32628/IJSRST229245

Keywords:

Naïve Bayes, Natural Language Processing (NLP), Real News, Fake News, Term Frequency Inverse Document Frequency (tfidf).

Abstract

In this paper considers the use of NLP (Natural Language Processing) methods for identifying Fake news, that is, deceptive reports that come from untrustworthy sources. Simply building a model based on a tally vectorizer (using word counts) or a (Term Frequency Inverse Document Frequency) tfidf framework (word counts compared to how frequently they're used in different articles in your dataset) can only get you so far. However, these models do not take into account critical aspects like word requesting and setting. It is entirely possible that two articles that are similar in their promise include will be completely different in their significance. The information science community has reacted by taking action against the problem. There is a competition called the "Fake News Challenge," and Facebook is using AI to sift fake reports through client channels. Combating Fake News is an excellent book arrangement project with a simple recommendation. Is it possible for you to build a model that can distinguish between "Genuine" and "Fake" news? As a result, a proposed work on amassing a dataset of both fake and genuine news and using a Naive Bayes classifier to create a model to classify an article as fake or genuine based on its words and expressions.

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International Journal of Scientific Research in Science and Technology

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Published

2022-04-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

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How to Cite

[1]
Vaishali Singh "A Review of Machine Learning-Based Fake News Analysis" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 2, pp.252-261, March-April-2022. Available at doi : https://doi.org/10.32628/IJSRST229245