Radio remains one of the most-consumed forms of traditional media today, with 89% of Americans listening to radio at least once a week as of 2018, a number that is actually increasing during the pandemic. format and 60% of Americans trust radio news to “deliver timely information about the current COVID-19 outbreak.”
Local talk radio is home to a diverse assortment whatsapp lead of personality-driven programming that offers unique insights into the concerns and interests of citizens across the nation. Yet radio has remained stubbornly inaccessible to scholars due to the technical challenges of monitoring and transcribing broadcast speech at scale.
Debuting this past July, the Internet Archive’s Radio Archive uses automatic speech recognition technology to transcribe this vast collection of daily news and talk radio programming into searchable text dating back to 2016, and continues to archive and transcribe a selection of stations through present, making them browsable and keyword searchable.
Ngrams data set
Building on this incredible archive, the GDELT Project and I have transformed this massive archive into a research dataset of radio news ngrams spanning 26 billion English language words across portions of 550 stations, from 2016 to the present.
You can keyword search all 3 million shows, but for researchers interested in diving into the deeper linguistic patterns of radio news, the new ngrams dataset includes 1-5grams at 10 minute resolution covering all four years and updated every 30 minutes. For those less familiar.