• Project Title: Determining Political Bias and Reliability with Natural Language Processing

  • BASIS Advisor: Dr. Brown

  • Onsite Mentor: Mr. Rob Liu

Although the internet age has made it easier for people to keep up with current news, people must be able to differentiate between false and true news. The way news sources (i.e. CNN, Fox News) report stories or the details news sources choose to present can subconsciously sway readers to a certain opinion. In an era where political falsehoods may be proliferated much faster than true news, readers must have some way to judge for themselves whether they trust a news story or not. In response, websites like the Official Media Bias Fact Check and Snopes try to offer expert opinions and evaluations to determine whether a news story is reliable or not, but these websites have been accused of inciting their own political stances into their analysis and not being transparent. This project focuses on exploring the problem from a computer science aspect: Can Natural Language Processing be used to determine the reliability and political biases of popular media sources by analyzing the stories they report? By grouping news articles intro topically related categories and analyzing the magnitude of their political bias and their validity, my project aims to make conclusions on how reliable the top 15 most accessed news sources are.