Classifying Fake News Articles Using Machine Learning Techniques
DOI:
https://doi.org/10.5281/zenodo.5762114Keywords:
Classifiers, Confusion Matrix, Count Vectorizers, Machine Learning, Exploratory Data AnalysisAbstract
News is important in daily life as it informs our view to the world and in response, we take actions and make choices in various aspects. Gradually, the tendency towards online news is increasing as it is more concise and is available at the finger tips. There has been large generation of deceptive content worldwide that has an effect on the formation of opinions, making decisions and voting trends. Most of the 'fake news' is initially circulated through social media networks such as Twitter, Facebook, and then makes the way into mainstream media outlets such as Radio and TV. The fake news articles share linguistic features such as heavy use of quoted material. In this use case, the results of fake news detection test and the performance of fake news classifiers is discussed. The aim is to build a new fake news detector using classifiers like Logistic Regression, Decision tree classification, Multinomial Naive Bayes classification.
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Copyright (c) 2021 Doddi Srilatha, Dustakar Prasanth Rao, Chukka Nikhil, Janumpally Sai Teja
This work is licensed under a Creative Commons Attribution 4.0 International License.
