Infringement of Tweets Geo-Location Privacy: an approach based on Graph Convolutional Neural Networks

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.