The project is an investigation into the apocalyptic narrative of the most-watched show on cable news. The backbone of the story is a rigorous data effort conducted by a team of Times reporters. It definitively and quantitatively demonstrates that the show has been a platform for legitimizing white nationalism and other extremist ideologies since it began in 2016.
The Times’s unprecedented reporting takes readers on a journey into the conspiratorial worldview that Mr. Carlson paints night after night for his more than three million viewers. The systematic analysis captures the scope and scale of the dangers posed by the show.
The data investigation became the [framing](https://www.nytimes.com/2022/04/30/us/tucker-carlson-gop-republican-party.html?action=click&module=RelatedLinks&pgtype=Article) of The Times’s Tucker Carlson series, which also included two narrative stories. It provided the evidence needed to unequivocally characterize the show as promoting white nationalism and other extremist ideologies. The data project was one of the most-read articles on The Times’s website the week it was published and has been widely cited by government officials, advocacy organizations and the news media as the authoritative source for understanding the dangers that Tucker Carlson poses. Senator Chuck Schumer referenced the project’s conclusions in a [letter](https://www.democrats.senate.gov/newsroom/press-releases/in-new-letter-to-rupert-murdoch-and-fox-news-executives-schumer-implores-fox-news-to-immediately-cease-dissemination-of-racist-great-replacement-theory-echoed-by-buffalo-mass-murderer) after the Buffalo mass shooting urging Rupert Murdoch and other Fox News executives to acknowledge “their role in the insidious spread of false far-right, white nationalist conspiracy theories like the ‘Great Replacement Theory.’” The analysis was also [cited](https://www.rules.senate.gov/imo/media/doc/Nelson%20Testimony.pdf) in a Senate hearing about threats to democracy. Amy Spitalnick, executive director of Integrity First for America, a pro-democracy group, lauded the investigation for putting “concrete numbers behind Tucker Carlson’s normalization of white supremacist Replacement Theory.” Rachel Maddow [praised The Times](https://www.vanityfair.com/news/2022/08/exclusive-rachel-maddow-gives-her-first-interview-as-she-steps-back) for deconstructing the show. The Poynter Institute for Media Studies commended the “incredibly detailed reporting.”
In order to trace the origins of Mr. Carlson’s rhetoric, the team systematically reviewed language used by the most extreme fringes of society and consulted experts in authoritarianism and propaganda about the findings. From this, they created a strict methodology for the data collection so that an apple-to-apples analysis would be possible.
The team then got to work watching and reading the transcripts of every show Tucker Carlson hosted from Nov. 14, 2016, through 2021. Video clips of the show were downloaded from the Internet Archive’s TV News Archive. Transcripts were pulled from LexisNexis. Segments were manually categorized and time-stamped in Google Sheets and meticulously sourced in corresponding Google Documents.
The team also counted the number of guests appearing in the 1,150 episodes based on whether the person agreed or disagreed with Mr. Carlson. The amount of time that Mr. Carlson spent speaking directly into the camera, uninterrupted, was also recorded, then the number of words were counted to track the length of his monologues.
Context about the project:
Tucker Carlson made headlines in April 2021 for promoting white replacement theory. Many believed it was the first time he had done so. A small group of reporters and editors began to think about creative ways to capture and quantify Mr. Carlson’s racism and divisiveness. They wanted to go beyond just showing anecdotal examples of Mr. Carlson’s extreme language, which other news organizations had already done.
Times journalists explored ways to do the investigation programmatically, through computer-driven language processing, but decided that this approach would not allow for the kind of nuanced, contextual analysis that would recognize the subtleties of his rhetorical manipulation. They also considered reviewing a subset of the shows, but decided that only a sweeping, comprehensive survey would be authoritative.
So they started watching and encoding every episode, building their own thorough database by hand. The data team’s unprecedented reporting revealed that Mr. Carlson had persistently pushed racist, misogynistic and anti-Semitic theories since his show launched in November 2016, correcting a public misperception that the show had only recently become more extreme.
Watching about 700 hours of the show and reading thousands of pages of transcripts was challenging in itself. It required perseverance over more than six months for several Times journalists. To put this in context, it would take a single person five months to continuously watch 700 hours of a show during normal work hours. And encoding data while watching quickly increases the time needed because of the need to go back and watch portions to ensure they were recorded correctly.
Once the team assembled the entire database, the challenge shifted to narrowing down the thesis, to present the massive dataset as a story that readers could easily follow. The team tried several different story forms, working through numerous storyboards. The team landed on an interactive form that would show the breadth of the reporting and let readers experience what it’s like to watch the show every night.
In crafting the narrative, the journalists took extra care to make sure that the piece was not amplifying Mr. Carlson’s rhetoric, but rather explaining his techniques while clearly letting readers know fact from fiction.
What can other journalists learn from this project?
Sometimes it’s necessary to build a dataset from scratch when one is not available. Automated techniques have the potential to make large data collection easier, but some data projects need human curation. The experience of collecting the data yourself can lead to unexpected insights that you might miss if you have too much distance from the raw material.