How ‘human-assisted reporting’ could help journalist find big stories

Derek Willis’ first 1A byline at The New York Times was for a story reporting that big donors aren’t contributing to President Barack Obama at the pace they did in the 2008 election cycle.

That story was done in the traditional manner of computer-assisted reporting, with Willis using the Times’ extensive database of campaign finance data to substantiate the story that repoter Nicholas Confessore was working on.

But it got him thinking about how journalists could use computer-assisted reporting to unearth stories rather than prove hypotheses. He explained on his blog:

Way too often, across topics and beats, we remain unaware of or ignore practices that could help us spot news and make sense of the larger picture. …

Many of the stories relating to campaign contributions, for example, are a result of reporters meticulously poring over pages of filings, applying the Potter Stewart test: “I’ll know it when I see it.”

Rather than relying on serendipity to uncover stories, Willis wrote, journalists should adopt a more systematic approach that seeks out things that could be newsworthy.

That’s how Willis learned earlier this month that a nonprofit had made an unusual $500,000 donation, which was quickly refunded, to a super PAC. The Times’ software for processing Federal Election Commission disclosures, which Willis built, alerts him when it spots potentially newsworthy events such as large donations, amendments to previous filings, and last-minute disclosures filed just before elections.

Ben Welsh, database producer for the Los Angeles Times, called this method “human-assisted reporting” in a talk last weekend at the International Symposium on Online Journalism.

Most journalists use computer-assisted reporting as a rifle, he said, a way to hunt down the story. But it’s also possible to set up systems that scan sets of data to look for stories that haven’t been revealed.

“If we can up our game in what we do in computer-assisted reporting, we don’t have to go out hunting for the story,” Welsh said. “The computer can go hunt it for us, right, and bring it back to us.”

Welsh described how he’s doing this at the Los Angeles Times. Every day, the police department emails him a file listing all the arrests for the previous day. His software looks for unusual things — high bail amounts that signal a major crime, for instance, and certain occupations like musician, producer or minister — and emails the results to police reporters at the Times.

This early-warning system means that the cops reporters aren’t reliant on the public-information officers or tipsters. It enabled the Times to be first with the story that “Puck” (you know, from “The Real World”) had been arrested last summer, and it tipped the Times off that the police had arrested new suspects in the brutal beating of a Giants fan at Dodgers Stadium last year.

“They didn’t want to tell the media right away who they were going to go busting,” Welsh said. “But I had the data. I didn’t have to ask the PIO. It was in my system, and it arrived at 2 in the morning, and we were the first reporters knocking on those neighbors’ doors, figuring out who these guys were.”

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