  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.
