Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity
This addresses the issue of biased media coverage for society and media organizations, but it is incremental as it builds on existing bias detection methods.
The paper tackled the problem of detecting political bias and unfairness in news articles by developing a neural model on a corpus of 6,964 labeled articles, finding insightful bias patterns at various text granularity levels.
Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society. Any form of media can contain overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such form of imbalanced news coverage can be exposed. The research presented in this paper addresses not only the automatic detection of bias but goes one step further in that it explores how political bias and unfairness are manifested linguistically. In this regard we utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com and develop a neural model for bias assessment. By analyzing this model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.