Fake News Detection Using Machine Learning Approaches

Content Provider	Scilit
Author	Khanam, Z. Alwasel, B. N. Sirafi, H. Rashid, M.
Copyright Year	2021
Description	Journal: Iop Conference Series: Materials Science and Engineering The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. This paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake news as true or false, by using tools like python scikit-learn, NLP for textual analysis. This process will result in feature extraction and vectorization; we propose using Python scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tiff Vectorizer. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest precision, according to confusion matrix results.
Related Links	https://iopscience.iop.org/article/10.1088/1757-899X/1099/1/012040/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/1099/1/012040
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	1
Volume Number	1099
Language	English
Publisher	IOP Publishing
Publisher Date	2021-03-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Cybernetical Science
Content Type	Text
Resource Type	Article