A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017
This work addresses the problem of scaling media framing studies for researchers and analysts by enabling computational analysis of massive news datasets, though it is incremental as it builds on existing framing detection methods.
The researchers tackled the challenge of scaling media framing analysis by developing a state-of-the-art classifier to systematically analyze 1.5 million New York Times articles from 2000 to 2017, revealing that short-term frame fluctuations align with major events and long-term trends like the increasing prevalence of the 'Cultural identity' frame.
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.