A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017

TL;DR

This paper introduces a scalable media frame classifier and applies it to analyze 1.5 million New York Times articles from 2000 to 2017, revealing temporal trends and patterns in media framing related to major events and specific topics.

Contribution

The study develops a state-of-the-art media frame classifier and demonstrates its application on a large dataset, uncovering long-term trends and event-driven fluctuations in media framing.

Findings

Frame fluctuations align with major events

Increasing prevalence of 'Cultural identity' frame over time

Distinct framing patterns in mass shootings

Abstract

Framing is an indispensable narrative device for news media because even the same facts may lead to conflicting understandings if deliberate framing is employed. Therefore, identifying media framing is a crucial step to understanding how news media influence the public. Framing is, however, difficult to operationalize and detect, and thus traditional media framing studies had to rely on manual annotation, which is challenging to scale up to massive news datasets. Here, by developing a media frame classifier that achieves state-of-the-art performance, we systematically analyze the media frames of 1.5 million New York Times articles published from 2000 to 2017. By examining the ebb and flow of media frames over almost two decades, we show that short-term frame abundance fluctuation closely corresponds to major events, while there also exist several long-term trends, such as the gradually increasing prevalence of the ``Cultural identity'' frame. By examining specific topics and sentiments, we identify characteristics and dynamics of each frame. Finally, as a case study, we delve into the framing of mass shootings, revealing three major framing patterns. Our scalable, computational approach to massive news datasets opens up new pathways for systematic media framing studies.