National broadcast outlets reflect the growing political polarization in America growing divides in social media. Fox News and CNN are two large broadcast channels that have shown partisan and inflammatory broadcast coverage, according to Virginia Tech researchers.
“When we’re talking about language that is mediatized and played over and over and over by actors who are influential, how does that affect the way the public talks about important social issues? Rigorous analysis of big data sets like this opens whole new avenues of understanding media and its impact,” said Eugenia Rho, an assistant professor in the Department of Computer Science.
Journalist turned professor Mike Horning studies how misinformation affects civic debate and political participation and collaborates with computer science researchers to analyze large amounts of data.
“Historically, if we were to tackle a question that was about media bias in the past 40 years, it’s going to be somebody looking at a data set of maybe 500 articles. That is very limited,” Horning said. “But computer scientists are now able to help us tackle some of these tough, tough questions by analyzing massive amounts of data, which we’ve never been able to do before. So that’s why I think it’s super cool to be able to work with them.”
Using a branch of artificial intelligence called natural language processing, the researchers analyzed nearly 300 billion words spoken on CNN and Fox News and nearly 133,000 tweets from the networks and their followers to determine if national broadcast news contained partisan and inflammatory content. They also looked at how that partisanship changed over time and if partisan content affected debate among the networks’ followers on social media.
The data sets
- Transcribed closed captions from news shows that were broadcast 24 hours a day, seven days a week by CNN and Fox News from Jan. 1, 2010, to Dec. 31, 2020.
- All tweets between 2010 and 2020 that were written by users who followed both @CNN and @FoxNews, mentioned or replied to either @CNN or @FoxNews, and contained keywords associated with six politically contentious topics: racism, Black Lives Matter, police, immigration, climate change and health care.
Americans get their news from television five times more often than from online and print outlets, the researchers wrote, and viewers are more likely to choose their news source based on partisan views.
Rho and Horning found the language used in broadcast coverage also predicts how viewers debate important national issues on social media.
The analysis showed that viewers of CNN and Fox News not only hold different political views but interpret the same words very differently. Words such as “illegal,” “enforcement” and “order” often came up in immigration discussions on Fox News; whereas CNN used words such as “parents,” “family,” “children,” “daughter” and “communities.” When discussing racism, CNN coverage often mentioned “protests,” while on Fox “crime” often came up.
The data shows that audiences on Twitter, or X, echoed the same language patterns of the broadcast news they favored. And vice versa.
Different audiences spend their media time in partisan echo chambers that reinforce their existing views, the researchers found.
“This country was founded upon the Declaration of Independence. Words have immense, immense power, and a tangible impact on people’s lives,” Rho said. “When we have this consistent pattern in which major broadcast networks diverge completely, to the extent that they’re portraying an almost different reality in which topics are discussed, then you have this irreconcilable division across audiences.”
There may be no simple solution.
“Part of the motivation for it has been the increasing decline of viewership in cable news. They are competing against everything on the web,” Horning said. “How can you cut through all the noise? The solution often is to become more outlandish and more rowdy. Because TV news is driven by ratings, the incentive is to make market-driven decisions that probably are not democratic.”
Convincing broadcasters to change their business model could be difficult. The research could help viewers make more informed media choices.
“As a computer scientist, being able to show this pattern at scale, I hope that it generates the necessary conversations around what’s good for the collective society,” Rho said. “Because if people just can’t be on the same page to talk about important issues, where do we go from here?”