Analyzing Who and What Appears in a Decade of US Cable TV News
This work addresses the problem of media representation and its influence on public opinion for researchers and the general public, providing a large-scale dataset and tool for analysis.
The researchers analyzed nearly 244,038 hours of video from three U.S. cable TV networks from 2010 to 2019 to study who appears on the news and what stories are covered, finding that male-presenting individuals received 2.4 times more screen time than female-presenting individuals in 2010 and 1.9 times in 2019.
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.