In August of 2020, the Stanford Cable TV News project officially launched after years of research and development. Now, those labors are paying off, as major news organizations begin to use the tool as a source for their reporting.
Just this month, the Washington Post has released two stories relying on its data and analysis. The first piece, titled The Trump media era ends not with a wow but a whisper, used the tool to highlight how major TV news networks have been providing very little airtime to Trump since leaving office. The second piece, titled Given the dire allegations against him, Matt Gaetz is very well-insulated, highlights the degree to which certain networks provide airtime to Matt Gaetz before and after allegations against him became public. The analysis is striking, showing large daily airtime on Fox News leading up to the scandal, with little coverage on CNN and MSNBC, only to be followed by a stark inverse following the announcement of the investigation into Gaetz.
This is a terrific application of the TV News tool, and represents analysis that would be difficult to imagine without its development. If you’d like to use the Stanford Cable TV News tool, check out the project at tvnews.stanford.edu. Please direct any questions to email@example.com.
More about the project below
Each day, cable TV news networks determine what information millions of Americans receive. They also set the context and tone of the information presented. Editorial decisions, about who appears on cable TV news and what they talk about, shape public opinion and culture. Many newsrooms and monitoring organizations, like Media Matters, American University’s Sunday Morning Monitor Archive, and NPR’s On The Media, routinely audit the content of news broadcasts, but these efforts involve the tedious and time-consuming work of manually counting who and what is on air.
To increase transparency around daily editorial choices, the Stanford Cable TV News Analyzer (tvnews.stanford.edu) uses modern AI techniques to automatically measure who is on the news and what they talk about. The tool leverages computer vision to detect faces, identify public figures, and estimate characteristics such as gender to examine news coverage patterns. To facilitate topic analysis the transcripts are time-aligned with video content and compared across dates, times of day, and programs.