But the algorithms the Obama campaign used in 2008—and that Mitt Romney has used so far this year—have trouble picking up voter positions, or the intensity around those positions, with much nuance. In other words, the analysts were getting pretty good at sorting the orange juice drinkers from the grapefruit juice drinkers. But they still didn’t have a great sense of why a given voter preferred grapefruit to O.J.—and how to change his mind. Polls seemed unable to get at an honest hierarchy of personal priorities in a way that could help target messages. Before the 2008 Iowa caucuses, every Democrat’s top concern seemed to be opposition to the Iraq war; once Lehman Bros. collapsed not long after the conventions, the economy became the leading issue across demographic and ideological groups. But microtargeting surveys were unable to burrow beneath that surface unanimity to separate individual differences in attitudes toward the war or the economy. If a voter writes in a Web form that her top concern is the war in Afghanistan, should she should be asked to enlist as a “Veterans for Obama” volunteer, or sent direct mail written to placate foreign-policy critics?
Campaigns do, however, take in plenty of information about what voters believe, information that is not gathered in the form of a poll. It comes in voters’ own words, often registered onto the clipboards of canvassers, during a call-center phone conversation, in an online signup sequence or a stunt like “share your story.” As part of the Dreamcatcher project, Obama campaign officials have already set out to redesign the “notes” field on individual records in the database they use to track voters so that it sits visibly at the top of the screen—encouraging volunteers to gather and enter that information. And they’ve made the field large enough to include the “stories” submitted online. (One story was 60,000 text characters long.)
What can the campaign do with this blizzard of text snippets? Theoretically, Ghani could isolate keywords and context, then use statistical patterns gleaned from the examples of millions of voters to discern meaning. Say someone prattles on about “the auto bailout” to a volunteer canvasser: Is he lauding a signature domestic-policy achievement or is he a Tea Party sympathizer who should be excluded from Obama’s future outreach efforts? An algorithm able to interpret that voter’s actual words and sort them into categories might be able to make an educated guess. “They’re trying to tease out a lot more nuanced inferences about what people care about,” says a Democratic consultant who worked closely with Obama’s data team in 2008.
Obama’s campaign has boasted that one of their priorities this year is something they’ve described only as “microlistening,” but would officially not discuss how they intend to deploy insights gleaned from their new research into text analytics. “We have no plans to read out our data/analytics/voter contact strategy,” spokesman Ben LaBolt writes by email. “That just telegraphs to the other guys what we're up to.”
Yet those familiar with Dreamcatcher describe it as a bet on text analytics to make sense of a whole genre of personal information that no one has ever systematically collected or put to use in politics. Obama’s targeters hope the project will allow them to make more sophisticated decisions about which voters to approach and what to say to them. “It’s not about us trying to leverage the information we have to better predict what people are doing. It’s about us being better listeners,” says a campaign official. “When a million people are talking to you at once it’s hard to listen to everything, and we need text analytics and other tools to make sense of what everyone is saying in a structured way.”