  Cable TV news reaches millions of U.S. households each day, meaning that
decisions about who appears on the news and what stories get covered can
profoundly influence public opinion and discourse. We analyze a data set of
nearly 24/7 video, audio, and text captions from three U.S. cable TV networks
(CNN, FOX, and MSNBC) from January 2010 to July 2019. Using machine learning
tools, we detect faces in 244,038 hours of video, label each face's presented
gender, identify prominent public figures, and align text captions to audio. We
use these labels to perform screen time and word frequency analyses. For
example, we find that overall, much more screen time is given to
male-presenting individuals than to female-presenting individuals (2.4x in 2010
and 1.9x in 2019). We present an interactive web-based tool, accessible at
https://tvnews.stanford.edu, that allows the general public to perform their
own analyses on the full cable TV news data set.
