Fake News Detection to Eradicate Yellow Journalism

Fake News Detection to Eradicate Yellow Journalism

1 A.Akshay Deep, 2 T.Akshay Goud, 3 P.Akshaya Reddy, 4 Akshaya sadrollu,

5 S. Akshaya , 6 L.Akhil Goud ,7 Dr.D.Thiyagarajan

12345 Students, 6 Assistant Professor

Artificial Intelligence and Machine Learning.

Department Of Computer Science And Engineering.

Malla Reddy University, Hyderabad, Telangana, India.

ABSTRACT :
In the modern era where the internet is ubiquitous, everyone relies on various online resources for news. Together with the increase in the use of social media platforms like Whatsapp, Facebook, Twitter, etc.. The need for an hour is to stop the spread of articles in developing countries like India.This Project comes up with the three applications of Natural Language Processing techniques for detecting 'fake news, that is, misleading news stories that come from unknown sources. Only by building a model based on a count vectorizer or a TF-IDF matrix, can only get you so far. But these models don’t consider the important qualities like word ordering and context. This process will result in feature extraction; we propose using Python sci-kit-learn library to perform extraction of text data because this library contains useful tools like Count Vectorizer, Tf-IDF Vectorizer, and Hashing Vectorizer.Then, we will perform the training model and then classify with the user input then it will obtain the highest accuracy and precision according to confusion matrix results.

Keywords : Fake News, Passive Aggressive Classifier, Machine Learning, Natural Language Processing