• BHANU JOSHI
• SARAH VEJLANI
• SIDDHANT DUBEY
• NEELAM PHADNIS

The widespread increase in fake news, whether created by humans or machines, has a negative impact on society and individuals on both a political and social level. The rapid rotation of news in the age of social media makes it difficult to assess its authenticity quickly. As a result, automated fake news identification tools have become a necessity. To solve the aforementioned problem, a hybrid Neural Network architecture is used, which incorporates the capabilities of CNN and LSTM, as well as two separate dimensionality reduction methods, PCA and Chi-Square. We'll use data from the Fake News Challenges (FNC) website, which includes four different forms of stances: agree, disagree, discuss, and unrelated. The aim of this study is to figure out what a news article's body is in relation to its headline using different deep learning and ML models.

Fake News, Machine Learning, Natural Language Processing, Deep Learning, Multi-Layer Perceptron, Long Short-Term Memory

"AUTOMATED MULTI-MODEL FAKE NEWS CLASSIFIER ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.212-221, October-2021, Available :http://www.jetir.org/papers/JETIRFD06032.pdf

"AUTOMATED MULTI-MODEL FAKE NEWS CLASSIFIER ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. pp212-221, October-2021, Available at : http://www.jetir.org/papers/JETIRFD06032.pdf