Workshop Proceedings of the 17th International AAAI Conference on Web and Social Media

Workshop: Mediate 2023: News Media and Computational Journalism Workshop

DOI: 10.36190/2023.25

Published: 2023-06-01
Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines
Jinsheng Pan, Weihong Qi, Zichen Wang, Hanjia Lyu, Jiebo Luo

There is a broad consensus that news media outlets incorporate ideological biases in their news articles. However, prior studies on measuring the discrepancies among media outlets and further dissecting the origins of thematic differences suffer from small sample sizes and limited scope and granularity. In this study, we use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022 to thoroughly track and dissect the fine-grained thematic discrepancy in U.S. news media. We employ multiple correspondence analysis (MCA) to quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs in order to derive a more holistic analysis. Additionally, we compare the most frequent n-grams in media headlines to provide further qualitative insights into our analysis. Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias. Meanwhile, the discrepancy in reporting foreign affairs is largely attributed to the diversity in individual journalistic styles. Finally, U.S. media outlets show consistency and high similarity in their coverage of economic issues.