Location data can reveal Twitter users’ homes, workplaces
New York, May 18 (IANS) Have you activated Twitter’s location-reporting feature as you post, tag or comment on the micro-blogging site? These location stamps on just eight Twitter posts a day can reveal your home or workplace address to even low-tech snoopers.
Researchers from Massachusetts Institute of Technology (MIT) and Oxford University have shown that location stamps on just a handful of Twitter posts can be enough to disclose the addresses of the poster’s home and workplace to a relatively low-tech snooper.
The tweets themselves might be otherwise innocuous — links to funny videos, say, or comments on the news.
The location information comes from geographic coordinates automatically associated with the tweets.
Twitter’s location-reporting service is off by default, but many Twitter users choose to activate it.
“Many people have this idea that only machine-learning techniques can discover interesting patterns in location data,” said Ilaria Liccardi, research scientist at MIT’s Internet Policy Research Initiative.
They feel secure that not everyone has the technical knowledge to do that.
“With this study, what we wanted to show is that when you send location data as a secondary piece of information, it is extremely simple for people with very little technical knowledge to find out where you work or live,” Liccardi noted.
In their study, Liccardi and her colleagues — Alfie Abdul-Rahman and Min Chen from Oxford — used real tweets from Twitter users in the Boston area.
The users consented to the use of their data, and they also confirmed their home and work addresses, their commuting routes and the locations of various leisure destinations from which they had tweeted.
The time and location data associated with the tweets were then presented to a group of 45 participants who were asked to try to deduce whether the tweets had originated at the Twitter users’ homes, their workplaces, leisure destinations or locations along their commutes.
The participants were recruited in Oxford to eliminate biasing that might result from familiarity with Boston geography. Similarly, they had no information about the content of the tweets.
The data were presented in three different forms – a static Google map, an animated version of the same map and resolutely low-tech version.
The maps featured only street names, with no names of businesses, parks, schools or other landmarks.
Predictably, participants fared better with map-based representations, correctly identifying Twitter users’ homes roughly 65 percent of the time and their workplaces at closer to 70 percent.
Across all three representations, participants with five days’ worth of data could correctly identify workplaces, for example, with more than 85 percent accuracy.
“The new study is an effort to help raise awareness about just how much privacy people may be giving up when they use social media,” the authors noted in a paper presented at the Association for Computing Machinery’s Conference on Human Factors in Computing Systems in California recently.