Researchers use Twitter to predict certain crimes by reading geo-tags
Data about locations and actions can point to greater likelihood of certain offences
Hidden in the Twittersphere are nuggets of information that could prove useful to crime fighters - even before a crime has been committed.
Researchers at the University of Virginia in the United States demonstrated tweets could predict certain kinds of crimes if the correct analysis is applied.
A research paper published in the scientific journal Decision Support Systems last month said the analysis of geo-tagged tweets could be useful in predicting 19 to 25 kinds of crimes, especially for offences such as stalking, thefts and certain kinds of assault.
The results are surprising, especially when one considers that people rarely tweet about crimes directly, said lead researcher Matthew Gerber of the university's Predictive Technology Lab.
Gerber said even tweets that have no direct link to crimes may contain information about activities often associated with them.
"What people are tweeting about are their routine activities," Gerber said. "Those routine activities take them into environments where crime is likely to happen.
"So if I tweet about getting drunk tonight, and a lot of people are talking about getting drunk, we know there are certain crimes associated with those things that produce crimes. It's indirect."
For the study, Gerber and his colleagues analysed tweets from the city of Chicago tagged to certain neighbourhoods, measured by individual square kilometers, and the city's crime database.
They then looked forward and were able to make useful predictions about areas where certain crimes were likely to occur, something that could be helpful in deploying police resources.
"This approach allows the analyst to rapidly visualise and identify areas with historically high crime concentrations," said the study.
"Future crimes often occur in the vicinity of past crimes, making hot-spot maps a valuable crime prediction tool."
In recent years, the idea of "predictive policing" has gained momentum as police departments rely on "big data" analytics from companies such as IBM.
This research comes on the heels of other studies showing how tweets can be analysed to predict elections, disease outbreaks and other events.
The study was funded by the US Army, which Gerber said used similar techniques to determine threats in places such as Iraq and Afghanistan.
There are limitations, Gerber noted. Adequate historical data is needed, and some kinds of crimes, such as kidnapping and arson, may not fall into the same patterns of predictability, for reasons the researchers could not explain.
Still, he said the New York police department had already contacted him, and he has begun to review data from that city to determine if the results from Chicago can be replicated.