Fake News Detection Using Neural Network

Abstract

This paper uses capabilities of deep learning algorithms Long Short-Term Memory (LSTM) models for the detection of hoax news. In today's world Fake news has become a major problem, as it can spread quickly and have a significant impact on public opinion. Traditional methods for detecting fake news have relied on fact-checking and manual verification, which are time-consuming and not always effective. With the increasing availability of news articles and social media posts, there is a need for automated methods for detecting fake news. One of the effective ways used to eradicate the fake news is adopting LSTM models that have shown considerable results in a variety of natural language processing tasks, including text classification and sentiment analysis. This paper describes how to use LSTM for fake news detection and evaluates its performance on a news article dataset.