• SREEGIRI S L
• RISHI KRISHNA SUGESH
• VYSHNAV V CHANDRAN
• SABIRA A S

The proliferation of misinformation has significant social and political consequences. In response, the development of automated tools for accurately classifying and distinguishing fake news from real news has become crucial. This paper presents a machine learning-based approach to identify fake news articles using a range of classification algorithms, including Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest classifiers. The models were trained and evaluated on a dataset comprising labeled news articles, utilizing Natural Language Processing (NLP) techniques for feature extraction. Preliminary results demonstrate the efficacy of the proposed methods in automating the detection of fake news, providing a scalable solution to mitigate the spread of false information.

"Fake news detection desktop application", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l598-l601, April-2024, Available :http://www.jetir.org/papers/JETIR2404B78.pdf

"Fake news detection desktop application", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl598-l601, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B78.pdf