Deep Learning and Natural Language Processing for fake news detection: A Survey

Abstract

Fake news is a term which deals with fallacy in information, content or some sort of statistics or facts revealed to public for some sort of attention, to abuse someone as a means for acquiring some benefits harming the other entity or to construct a territory of bloodshed among mankind. A survey found that 86% of users have been tricked upon by fake news out of which supreme disseminator is Facebook. Latest victim to this field was during the Citizen Amendment act (CAA) in 2020 where a survey by a team of news reporters showed that almost 95% of the protestors didn’t knew about that act and were indulged to think that their citizenships would be snatched and were a victim to a fake news. Various deep learning methods have been mentioned in this paper which focusses on breaking off the broadcasting of the vague news over the internet. Deep learning architectures have been touched upon as fake news detection accords with colossal amount of data. Several architectures like the artificial neural network which concentrates on classifying the text based news, convolutional neural network which deals with the text or image grouping of the updates the people receive online. They can be also used to verify the information based on the title or the source of data, other architectures like recurrent neural network can be looked upon to sight some unrecognizable patterns in the content with the aid of its segment long short term memory (LSTM).