Detecting Bias in News Article Content with Machine Learning

Abstract

The internet and its various social media platforms allow for the rapid spread of information. While this has a number of benefits for society, it can also facilitate the proliferation of misinformation. Especially in recent years, concerns have been raised over fake news, factually incorrect claims, and biased news articles, regarding the potential impact on society and resulting polarization. The fields of machine learning and natural language processing contain building blocks and tools with the potential to help address some of these issues. We explore the application of weak supervision of machine learning models to predict the bias and reliability of news articles based on their content. Training models under the assumption that all articles share the bias and reliability labels of the source of the articles, we test their capability to generalize to a more realistic set of individually labeled articles.