The study, published last week by the journal Science, identifies hardcore pro-Islamic State groups on social media by searching for key words, such as mentions of beheadings, and zeroing in on specific community pages and groups. These groups trade operational information, such as which drone is being used in an attack or how to avoid detection, as well as fundraising posts and extremist ideology.
An uptick in the creation of these groups correlated with terrorist attacks, the study found. When the team used the model they created on their set of data, they found that the model correctly predicted the Kobani attack they observed in 2014. [...]
"The data suggests that there's no such thing as a 'lone wolf' in that sense," Johnson said. "If an individual looks alone, the chance is that they will at some stage in the recent path have been in an aggregate. And if you look long enough, they will be in another aggregate soon." [...]
The study is not without its skeptics. "This is a potentially valuable approach, and more research should be done on the approach,” said J.M. Berger, a fellow in George Washington University’s Program on Extremism, told the New York Times. “But to jump ahead to the utility of it, I think, takes more work.”
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