An approach for Fake News Detection
Shreya Srivastava, Monika Srivastava, Shreya Jaiswal, Vaishnavi Malini, Chaynika Srivastava
Machine Learning, Fake News, TF--IDF, Passive Aggressive Classifier, Confusion Matrix
It has been called one of the most dangerous developments in modern history. Fake news, made-up stories that have been reported as real events, has become a new form of propaganda and misinformation. To combat the problem more effectively, our team has developed an automated system to detect fake news through a machine learning component. Most of the smartphone customers prefer to study the information through social media over the internet. The web sites publishing and providing the information also offer the supply of authentication. The query is the way to authenticate that information and articles which can be circulated amongst social media like WhatsApp groups, Facebook Pages, Twitter and different micro blogs & social networking sites. It is dangerous for society to consider rumors and fake information. The want of an hour is to forestall the rumors particularly in the growing and developing country like India, and consciousness on the correct, authenticated information articles. This paper demonstrates a version and the method for faux information detection. With the assistance of Machine Learning and Natural Language Processing, we have designed a Fake News Detection classifier model to determine whether or not the information is actual or faux with the usage of TF-IDF vectorizer and Passive Aggressive Classifier algorithm. The outcomes of the proposed version are in comparison with present models. The proposed version is running properly and defining the correctness of outcomes up to 93.6% of accuracy.
Article Details
Unique Paper ID: 154332

Publication Volume & Issue: Volume 8, Issue 10

Page(s): 577 - 581
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