Applying Social Network Extraction With Named Entity Recognition to the Examination of Political Bias Within Online News Articles

Abstract

We aim to expand the application of social network extraction with NER tools, which to date is largely limited to fiction. With the premise that news articles resemble mini-stories, this study explores the extraction of social networks from online United States news articles to examine relationships between political bias and network features. We find statistical significance with most trends, and find no substantial differences between Liberal and Conservative bias, but bias and neutrality. Furthermore, this study identifies several issues with social network analysis, proposing a more rigorous examination of textual characteristics that affect network features.