Nathan Ji¹ and Yu Sun², ¹USA, ²California State Polytechnic University, USA

Media sources, primarily of the political variation, have a hastening grip on narratives that can easily be constructed using biased views and false information. Unfortunately, many people in modern society are unable to differentiate these false narratives from real events. Utilizing natural language processing, sentiment analysis, and various other computer science techniques, models can be generated to help users immediately detect bias and falsehoods in political media. The models created in this experiment were able to detect up to 70% accuracy on political bias and 73% accuracy on falsehoods by utilizing datasets from a variety of collections of both political media and other mediums of information. Overall, the models were successful as the standard for most natural language processing models achieved only about 75% accuracy.

Data Science, Political Bias, Fake News, Supervised Machine Learning, and natural-language processing.