A computational analysis of agenda setting

Agenda setting theory explains how media affects its audience. While traditional media studies have done extensive research on agenda setting, there are important limitations in those studies, including using a small set of issues, running costly surveys of public interest, and manually categorizing the articles into positive and negative frames. In this paper, we propose to tackle these limitations with a computational approach and a large dataset of online news. Overall, we demonstrate how to carry out a large-scale computational research of agenda setting with online news data using machine learning.