Deep Learning Algorithms for Detecting Fake News in Online Text

Abstract

Spreading of fake news is a social phenomenon that is pervasive at the social level between individuals, and also through social media such as Facebook and Twitter. Fake news that we are interested in is one of many kinds of deception in social media, but it's more important one as it is created with dishonest intention to mislead people. We are concerned about this issue because we have noticed that this phenomenon has recently caused through the means of social communication to change the course of society and peoples and also their views, for example, during revolutions in some Arab countries have emerged some false news that led to the absence of truth and stirs up public opinion and also fake of news is one of the factors Trump successes in the presidential election. So we decided to face and reduce this phenomenon, which is still the main factor to choose most of our decisions. Techniques of fake news detection varied, ingenious, and often exciting. In this paper our objective is to build a classifier that can predict whether a piece of news is fake or not based only its content, thereby approaching the problem from a purely deep learning perspective by RNN technique models (vanilla, GRU) and LSTMs. We will show the difference and analysis of results by applying them to the dataset that we used called LAIR. We found that the results are close, but the GRU is the best of our results that reached (0.217) followed by LSTM (0.2166) and finally comes vanilla (0.215). Due to these results, we will seek to increase accuracy by applying a hybrid model between the GRU and CNN techniques on the same data set.