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Detection of Fake News using Machine Learning Models

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Velivela Durga Lakshmi, Ch Sita Kumari

Velivela Durga Lakshmi and Ch Sita Kumari. Detection of Fake News using Machine Learning Models. International Journal of Computer Applications 183(47):22-27, January 2022. BibTeX

	author = {Velivela Durga Lakshmi and Ch Sita Kumari},
	title = {Detection of Fake News using Machine Learning Models},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2022},
	volume = {183},
	number = {47},
	month = {Jan},
	year = {2022},
	issn = {0975-8887},
	pages = {22-27},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2022921874},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In the present-day scenario, it is becoming a big problem to find whether a piece of news is real or fake. It is causing great loss to the individual and organization. The news articles can be from news channels or any other sources. In this project, the fake news is detected based on text, title, and author as parameters and converting them into vectors using Term Frequency- Inverse Document Frequency (TF-IDF) and Count vectorizers. On the vectors, PCA was applied to reduce the dimensions. The reduced vectors were given as input to the supervised machine learning algorithms. The resultant performance of algorithms was analyzed based on accuracy, precision, and recall. Hence, Random Forest classifier along with Count vectorizer gives the best technique for detection of the authenticity of the news.


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Fake News, Machine Learning, SVM, Random Forest, Logistic Regression, Naïve Bayes.