Your friends’ tweets may be giving away more information about you than you realise, say sneaky computer scientists at the University of Rochester.
They’ve found they can determine a person’s location within a 100 meter radius with 85 percent accuracy, using only the location of that person’s friends.
They were also able to predict a person’s Twitter friendships with high accuracy, even when their profile was kept private.
The team studied the messages and data of heavy Twitter users from New York City and Los Angeles to develop a computer model for determining human mobility and location.
The users, who sent out 100 or more tweets per month, had public profiles and had enabled GPS location sharing. The location data of selected individuals was sampled over a two-week period – and then was ignored, as the researchers tried to pinpoint their locations using only the information from their Twitter friends.
And in more than eight out of ten instances, they successfully figured out where the individuals lived to within one city block.
“Once you learn about relationships from peoples’ tweets, it makes senses that you can track them,” says graduate student Adam Sadilek. “My fiancée may be a good predictor of my location because we have breakfast together every morning.”
The team then used the same data sets from New York and Los Angeles, but ran the models in reverse. They made full use of individuals’ location data and the content of their tweets, but ignored their lists of followers. The aim was to see if it was possible to predict a person’s friends.
And when they compared the predictions of their models with the actual network of friendships, the researchers found they were correct 90 percent of the time.
“If people spend a lot of time together online and talk about the same things,” said Sadilek, “they’re more likely to be friends.”
The team now plans to apply its models to such tasks as tracking and predicting the spread of communicable diseases. If people and their friends in one location tweet about having a fever and not feeling well, for example, it could indicate a flu outbreak.
