An Empirical Study on Readers' Intuition of the Political Stances in News Headlines

Prior research shows when reading news articles, most Americans solely read the headlines with less than half continuing to read the full story. However, it is unclear to what extent the readers can appreciate the framing of headlines, particularly when the news sources are unrevealed. In this work, political science is combined with natural language processing in order to analyze the effects of implicit bias within news headlines on the public. Americans of voting age annotated a dataset discerning their sentiment towards news headlines which was then leveraged by clustering and classification methods in order to understand the behaviors of the readers and how they are impacted by news headlines. Through the analysis of this dataset, I found that the interpretation of headlines is not always consistent with the self-identified political affiliation and that implicit bias affects both writer and reader.