We are planning to make more
Posted: Sat Jul 12, 2025 4:48 am
Beyond this experimental facial detection, we have big plans for the future. than a million hours of TV news available to researchers from both private and public institutions via a digital public library branch of the Internet Archive’s TV News Archive. These branches would be housed in computing environments, where networked computers provide the processing power needed to analyze large amounts of data.
Researchers will be able to conduct their own experiments using machine learning to extract metadata from TV news. Such metadata could include, for example, speaker identification–a way to buy sales lead identify not just when a speaker appears on a screen, but when she or he is talking. Researchers could create ways to do complex topic analysis, making it possible to trace how certain themes and talking points travel across the TV news universe and perhaps beyond. Metadata generated through these experiments would then be used to enrich the TV News Archive, so that any member of the public could do increasingly sophisticated searches.
Feedback! We want it
We are eager to hear from people using the Face-O-Matic Slack app and get your feedback.
Is the Face-O-Matic Slack app useful? What would make it more useful?
Would a structured data stream delivered via JSON, csv, and/or other means be helpful? What sort of information would you like to be included in such a data set?
Who is it important for us to track?
What else?
The weeds
The TV News Archive, our collection of 1.3 million+ TV news broadcasts dating back to 2009, is already searchable through closed captions.
But captions don’t always get you everything you want. If you search, for example, on the words “Donald Trump” you get back a hodge-podge of clips in which Trump is speaking and clips where reporters are talking about Trump. His image may not appear on the screen at all. The same is true for “Barack Obama,” “Mitch McConnell,” “Chuck Schumer,” or any name.
Researchers will be able to conduct their own experiments using machine learning to extract metadata from TV news. Such metadata could include, for example, speaker identification–a way to buy sales lead identify not just when a speaker appears on a screen, but when she or he is talking. Researchers could create ways to do complex topic analysis, making it possible to trace how certain themes and talking points travel across the TV news universe and perhaps beyond. Metadata generated through these experiments would then be used to enrich the TV News Archive, so that any member of the public could do increasingly sophisticated searches.
Feedback! We want it
We are eager to hear from people using the Face-O-Matic Slack app and get your feedback.
Is the Face-O-Matic Slack app useful? What would make it more useful?
Would a structured data stream delivered via JSON, csv, and/or other means be helpful? What sort of information would you like to be included in such a data set?
Who is it important for us to track?
What else?
The weeds
The TV News Archive, our collection of 1.3 million+ TV news broadcasts dating back to 2009, is already searchable through closed captions.
But captions don’t always get you everything you want. If you search, for example, on the words “Donald Trump” you get back a hodge-podge of clips in which Trump is speaking and clips where reporters are talking about Trump. His image may not appear on the screen at all. The same is true for “Barack Obama,” “Mitch McConnell,” “Chuck Schumer,” or any name.