How Twitter Could Help Police Departments Predict Crime

Eric Jaffe in Atlantic Cities: “Initially, Matthew Gerber didn’t believe Twitter could help predict where crimes might occur. For one thing, Twitter’s 140-character limit leads to slang and abbreviations and neologisms that are hard to analyze from a linguistic perspective. Beyond that, while criminals occasionally taunt law enforcement via Twitter, few are dumb or bold enough to tweet their plans ahead of time. “My hypothesis was there was nothing there,” says Gerber.
But then, that’s why you run the data. Gerber, a systems engineer at the University of Virginia’s Predictive Technology Lab, did indeed find something there. He reports in a new research paper that public Twitter data improved the predictions for 19 of 25 crimes that occurred early last year in metropolitan Chicago, compared with predictions based on historical crime patterns alone. Predictions for stalking, criminal damage, and gambling saw the biggest bump…..
Of course, the method says nothing about why Twitter data improved the predictions. Gerber speculates that people are tweeting about plans that correlate highly with illegal activity, as opposed to tweeting about crimes themselves.
Let’s use criminal damage as an example. The algorithm identified 700 Twitter topics related to criminal damage; of these, one topic involved the words “united center blackhawks bulls” and so on. Gather enough sports fans with similar tweets and some are bound to get drunk enough to damage public property after the game. Again this scenario extrapolates far more than the data tells, but it offers a possible window into the algorithm’s predictive power.

The map on the left shows predicted crime threat based on historical patterns; the one on the right includes Twitter data. (Via Decision Support Systems)
From a logistical standpoint, it wouldn’t be too difficult for police departments to use this method in their own predictions; both the Twitter data and modeling software Gerber used are freely available. The big question, he says, is whether a department used the same historical crime “hot spot” data as a baseline for comparison. If not, a new round of tests would have to be done to show that the addition of Twitter data still offered a predictive upgrade.
There’s also the matter of public acceptance. Data-driven crime prediction tends to raise any number of civil rights concerns. In 2012, privacy advocates criticized the FBI for a similar plan to use Twitter for crime predictions. In recent months the Chicago Police Department’s own methods have been knocked as a high-tech means of racial profiling. Gerber says his algorithms don’t target any individuals and only cull data posted voluntarily to a public account.”