I. Introduction
The main aim of this work is to develop a means for detecting the difference between reliable and unreliable news stories. After conducting research into the effect of fake news, it is clear that identifying unreliable news articles is critical. Just a few years ago the term "fake news" was meaningless, but today it is a big issue. Due to unreliable sources, such as DC Gazette, people either become misinformed or untrusting of the media. People now often believe the lies told in fake news articles and it is becoming increasingly difficult for the public to distinguish truth from falsehoods. Balmas [12] states "that perceived realism of fake news is stronger among individuals with high exposure to fake news and low exposure to hard news than among those with high exposure to both fake and hard news". Due to the fact that people believe these unfounded stories, or perhaps because of the shocking information they contain, fake news has been found to be very enduring. Lewandowsky et al. [13] discuss the "susceptibility of individuals to false and misleading information". It is seen, that even after stories are proven to be fake, people are often more likely to recall the fake articles about an event than the real facts, due to the shocking and attention-grabbing nature of the fake content.