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Leskovec says that these results are particularly powerful when combined with tools that can predict the volume of attention that a story will get, rather than just the pattern by which it will spread. To predict volume, the researchers look at where an item is published, its subject area, and other factors.

The research could be used to help sites manage their content, Leskovec says. For example, a large news site might use the approach to decide how long to give a story a prominent place on its front page.

Ilya Grigorik, CTO and cofounder of PostRank, a company that performs real-time analysis of topics and trends online, says the researchers’ findings agree with the data his company has collected. In particular, he notes that stories are most talked about within the first 24 hours. PostRank has observed that 50 percent or more of the attention a story gets happens within the first hour, and 80 percent or more happens within the first 24—numbers that Grigorik says have been consistent over the past three years.

Grigorik thinks that more fine-tuning would need to be done to make the work useful in practice. In particular, he thinks the shapes the researchers identified need more characterization, so that people can grasp what it means about a story for it to follow a particular shape.

News-aggregation sites might use a tool based on the research to predict how well posts will do, Grigorik says, although it’s unclear how much more effective that would be than using editorial judgment.

Jon Kleinberg, a professor of computer science at Cornell University who has worked with Leskovec in the past, says that the research is “a very promising approach for sorting out the different ways in which news draws attention over time.” He says he’s particularly interested in seeing the rise and fall of news stories classified in terms of time rather than topic and in exploring the complementary roles that blogs and mainstream news sources play in that news cycle.

Leskovec plans to do more research on how information spreads on the Internet. He and his colleagues are also looking into how information changes as it travels, possibly gaining insight into how rumors and inaccuracies are introduced.

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Credit: Stanford University

Tagged: Web, Internet, Twitter, media, network analysis

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