In the digital age where traditional journalism is shrinking, journalists are scattered. Local news outlets close without enough readers and finances to stay afloat; national publications cut jobs. Journalists’ workload continues to increase to accommodate these changes, but there is not much one human being can do.
Then comes artificial intelligence. Alexander Spangher, a graduate student at the University of Southern California’s Institute for Information Studies, set out to reduce that manual burden in a four-part project that automates many of the most tedious aspects of journalistic work.
Former data scientist for the New York Times, Spangher saw an opportunity to use his computer skills to help journalists drowning in the influx of information coming their way – while under pressure to make the right choice. In short: what is worth publishing and can AI weigh in?
“What we’re trying to do is take the really boring and mundane parts of the job and make it easier,” Spangher explained, “so reporters can pursue more stories, find more sources, use more sophisticated resources, promote their work on more platforms, and cover more relevant topics overall.
Spangher was inspired by the imperative role journalism plays in supporting democracy and the need to preserve it.
“If it weren’t for journalists telling us about our world, we wouldn’t understand our communities and know how to get involved. We wouldn’t be able to do informed votes,” Spangher said. “There is so much crucial and important information that journalists reveal to us. I am continually struck by the role of these journalists in liberal democracy. For example, one study found that every dollar spent by a news outlet brought in $1,000 to the company. Another found that local journalism was the main factor in promoting good government. »
The first aspect of the solution he designed involves training a model to determine the timeliness of information, using a log. The model tries to predict whether a piece of text will make headlines or end up in the middle or at the end.
“This ‘front page vs. not’ model only covers such a small slice of the spectrum of media interest — there are so many other layers, like stories, that aren’t covered at all. So we weren’t sure it would do anything useful. But, we applied it to material that journalists use all the time in their reporting – like the minutes of a Los Angeles City Council meeting – and found that we surfaced some really interesting leads! For example, one about the Los Angeles City Council enforcing pay equity between women’s and men’s soccer teams, which according to our algorithm should have been at the top of the first page, but hadn’t been covered yet” , explained Spangher. “There are all these important stories that reporters just haven’t had time to find and cover.”
Spangher adviser Jonathan May, an associate research professor of computer science at USC, says local news is being hit the hardest by the tech revolution, even though there’s a substantial need for it right now.
“A big problem with local journalism is that it’s hard to sustain even in the best of times,” May noted. Some areas are “news wastelands” and simply don’t get the coverage they used to, but with tools like these, “a local reporter’s limited bandwidth can be amplified.”
When it comes to the LA City Council meeting notes, for example, the goal is “to try to take these public records and get to the main aspects of the meeting that seem worth following.” , added May.
The second part of the project focuses on who to interview once a story idea is in place, equipping a template to “help journalists find sources they might not have considered, perhaps more diverse sources that can still meet their information needs”. Spangher highlighted.
One of the most time-consuming tasks for a journalist is to publish and promote their story on social media platforms. The objective of the third part of Spangher’s work is to develop a system capable of modifying a journalist’s article to promote it on various platforms such as Facebook, LinkedIn or Twitter. The AI assistant could formulate different versions of the article to get the best results and have the widest possible reach.
“The question at the end is what’s going to work on different platforms based on the dynamics of the platform, how it works, and what people pay attention to. All three are largely algorithmic,” Spangher explained. “So it makes sense that one algorithm can meet the requirements of another algorithm.”
The Last Piece of Spangher’s Work examines how the stories have grown — or haven’t grown — since they were published. He collected a large data set of various articles and their versions with the aim of “tracing the arc of a story over time”. Identifying trends and tracking “which stories will continue to grow and which will stop growing are important questions,” he stressed. He received an Outstanding Paper Award at NAACL 2022 for this work.
Spangher is adamant that despite the remarkable implications of these new tools, he believes AI will never replace the journalist. “The core of journalism is a fundamentally human process,” he said.
However, one of the most challenging aspects of this work, Spangher noted, was figuring out “how to think about the tools that we think are going to be useful without scaring off journalists or making them feel like machine learning take their job.”
The research is still in its infancy, but Spangher’s ultimate goal is to make these tools available in some form to news outlets and journalists around the world.
“In maybe five or ten years, I hope to explore the possibility of creating a startup similar to a service platform that really brings together all these tools and helps journalists throughout the story production pipeline,” said the doctoral student. .
Posted on November 28, 2022
Last updated November 28, 2022
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