AI Case Study
TV News Archive automatically identifies politicians' appearances on news programs using facial recognition
The Internet Archive's TV News Archive has implemented a facial recognition system developed with Matroid, called Face-O-Matic, to automatically identify certain US politicians' appearances on news programs. It does so using a machine learning system, and then captures this data for researchers and journalists to use to evaluate bias in airtime.
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From the Internet Archive blog: "Face-o-Matic is an experimental service, developed in collaboration with the start-up Matroid, that tracks the faces of selected high level elected officials on major TV cable news channels: CNN, Fox News, MSNBC, and the BBC. First launched as a Slack app in July, the TV News Archive, after receiving feedback from journalists, is now making the underlying data available to the media, researchers, and the public. Face-o-Matic uses facial recognition algorithms to recognize individuals on TV news screens. Face-o-Matic finds images of people when TV news shows use clips of the lawmakers speaking; frequently, however, the lawmakers’ faces also register if their photos or clips are being used to illustrate a story, or they appear as part of a montage as the news anchor talks."
"For every ten minutes that TV cable news shows featured President Donald Trump’s face on the screen this past summer, the four congressional leaders’ visages were presented for one minute, according an analysis of Face-o-Matic downloadable, free data fueled by the Internet Archive’s TV News Archive and made available to the public today."
"Detecting faces on TV news and turning them into data provides a new quantitative path for journalists and researchers to explore how news is presented to the public and compare and contrast editorial choices that individual networks make. This new measure shows us the duration that politicians’ faces are actually shown on screen, whether it’s a clip of that person speaking, muted footage, or a still photo shown in the background to illustrate a point. We expect this successful experiment will contribute to future approaches to tracking how messages travel on TV news, helping journalists, fact-checkers, researchers and more."