  The tremendous popularity gained by Online Social Networks (OSNs) raises
natural concerns about user privacy in social media platforms. Though users in
OSNs can tune their privacy by deliberately deciding what to share, the
interaction with other individuals within the social network can expose, and
eventually disclose, sensitive information. Among all the sharable personal
data, geo-location is particularly interesting. On one hand, users tend to
consider their current location as a very sensitive information, avoiding to
share it most of the time. On the other hand, service providers are interested
to extract and utilize geo-tagged data to offer tailored services. In this
work, we consider the problem of inferring the current location of a user
utilizing only the available information of other social contacts in the OSN.
For this purpose, we employ a graph-based deep learning architecture to learn a
model between the users' known and unknown geo-location during a considered
period of time. As a study case, we consider Twitter, where the user generated
content (i.e., tweet) can embed user's current location. Our experiments
validate our approach and further confirm the concern related to data privacy
in OSNs. Results show the presence of a critical-mass phenomenon, i.e., if at
least 10% of the users provide their tweets with geo-tags, then the privacy of
all the remaining users is seriously put at risk. In fact, our approach is able
to localize almost 50% of the tweets with an accuracy below 1km relying only on
a small percentage of available information.
