If you pay passing attention to the media landscape, you know that most mainstream news outlets have had their business models undermined by the digital revolution. As their general-interest monopolies have been pillaged by niche online competitors, traditional news organizations have lost revenue and cachet, laying off journalists in waves that have grown into tsunamis. This process has created dire prospects for the future of investigative reporting, often seen as the most costly of journalistic forms.

In the middle of November, Sam Zell, the occasionally foul-mouthed chief executive officer of Tribune Company, concisely summarized a common 21st-century media-titan view of public interest journalism in an interview by Joanne Lipman, editor in chief of Condé Nast's business magazine, Portfolio: "I haven't figured out how to cash in a Pulitzer Prize."

Now, though, the digital revolution that has been undermining in-depth reportage may be ready to give something back, through a new academic and professional discipline known in some quarters as "computational journalism." James Hamilton is director of the DeWitt Wallace Center for Media and Democracy at Duke University and one of the leaders in the emergent field; just now, he's in the process of filling an endowed chair with a professor who will develop sophisticated computing tools that enhance the capabilities — and, perhaps more important in this economic climate, the efficiency — of journalists and other citizens who are trying to hold public officials and institutions accountable.

In fact, Hamilton says, his interest in computational journalism grew out of his studies of media economics, which suggested that accountability journalism — the watchdog function that really can involve sorting through tens of thousands of documents, looking for the one smoking invoice — does not have the kind of everyday utility or broad interest value that will ever make it a revenue center in the disaggregated media world of the Internet. For every Watergate exposé that brings renown and readers to a Washington Post, there are dozens of other investigative reports — often equally time-consuming and expensive — that pass largely unnoticed, as the public mind focuses on the return of Britney or the current vogue in sexy vampires. (And if you don't think Britney and vampires are important, go find yourself a Dec. 11 copy of Rolling Stone, which was, once upon a distant time, the politico-cultural bible of the Watergate age.)

On a disaggregated Web, it seems, people and advertisers simply will not pay anything like the whole freight for investigative reporting. But Hamilton thinks advances in computing can alter the economic equation, supplementing and, in some cases, even substituting for the slow, expensive and eccentric humans required to produce in-depth journalism as we've known it.

Already, complex algorithms — programming often placed under the over-colorful umbrella of "artificial intelligence" — are used to gather content for Web sites like Google News, which serves up a wide selection of journalism online, without much intervention from actual journalists. Hamilton sees a not-too-distant future in which that process would be extended, with algorithms mining information from multiple sources and using it to write parts of articles or even entire personalized news stories.

Hamilton offers a theoretical example, taking off from EveryBlock, the set of Web sites masterminded by Adrian Holovaty, one of the true pioneers of database journalism and a former innovation editor at washingtonpost.com. If you live in one of the 11 American cities EveryBlock covers, you now can enter your address, and the site gives you civic information (think building permits, police reports and so on), news reports, blog items and other Web-based information, such as consumer reviews and photos, all connected to your immediate geographic neighborhood. In the not-too-distant future, Hamilton suggests, an algorithm could take information from EveryBlock and other database inputs and actually write articles personalized to your neighborhood and your interests, giving you, for example, a story about crime in your neighborhood this week and whether it has increased or decreased in relation to a month or a year ago.