Fake News Detection Using Natural Language Processing (NLP) Technology on Recent Famous Indian News

Abstract

It’s like social media for news consumption. On one hand, it’s inexpensive, convenient and quick to publish, resulting in people finding and consuming social media news. It also facilitates the disseminating of “fake news”, that is, fake news (that is, poorly reported, deliberately false content). The mass dissemination of fake news could have very harmful effects on the people and society. This makes social media fake news detection a recent new field of research that is very much in demand [1]. Digital media explosion and social media have encouraged fake news proliferation at great social, political and economic cost. Solution to this problem requires powerful and scalable automated detection. In this paper, we present a systematic investigation on the fake news detection with Natural Language Processing (NLP). We talk about how machine learning algorithms, linguistic feature extraction, and deep learning architectures are combined to create high performance systems for discovering and suppressing the propagation of misinformation. This work combines text and context feature analysis, modern models (such as BERT and LSTM), and various datasets to seek higher detection accuracy and scale. This data can be used to design automated solutions that could combat fake news in real-time, and provide feedback on technical issues as well as the way forward.