Radley Balko is to #Ferguson as Nate Silver was to 2012:
Not at all helpful (quite the opposite, actually):
On Thursday, a name released by the hacking collective Anonymous began circulating, but St. Louis County Police said that the name was inaccurate. The same kind of thing happened Wednesday, as another name began floating around and gaining traction. Ferguson police said that that name was also inaccurate, telling The Post that these reports were false.
A spokesman with the St. Louis County Police was critical of the group Anonymous for releasing the information.
“People really need to harshly judge the accuracy of this group, given that they’ve now given false information about several important things,” Sgt. Colby Dolly said on Thursday.
Dolly said that authorities were trying to locate the person identified by Anonymous on Thursday to warn him.
Of course, such recklessness could easily be prevented by, y’know, releasing the name of the shooter (even the NRO, etc).
Late night Hump Day rawk out continues unabated:
Bedtime rawkout in 3, 2, 1…
Via Charles Johnson: Cops in paramilitary gear are now pointing sniper rifles at peaceful black protesters in Ferguson, MO.
Tear gas and rubber bullets. Jeebus. (h/t)
Science: this is why we keep you around:
Nine days before the World Health Organization announced the African Ebola outbreak now making headlines, an algorithm had already spotted it. HealthMap, a data-driven mapping tool developed out of Boston Children’s Hospital, detected a “mystery hemorrhagic fever” after mining thousands of web-based data sources for clues.
“We’ve been operating HealthMap for over eight years now,” says cofounder Clark Freifeld. “One of the main things that has allowed it to flourish is the availability of large amounts of public event data being accessible on the Internet.”
As anyone who’s ever looked at the Internet knows, any bulk consumption of web content is bound to scoop up tons of noise, especially when sources like Twitter and blogs are involved. To cope with this, HealthMap applies a machine learning algorithm to filter out irrelevant information like posts about “Bieber fever” or uses of terms like “infection” and “outbreak” that don’t pertain to actual public health events.
“The algorithm actually looks at hundreds of thousands of example articles that have been labeled by our analysts and uses the examples to pick up on key words and phrases that tend to be associated with actual outbreak reports,” explains Freifeld. “The algorithm is continually improving, learning from our analysts through a feedback loop.”