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The tremendous popularity gained by Online Social Networks (OSNs) raisesnatural concerns about user privacy in social media platforms. Though users inOSNs can tune their privacy by deliberately deciding what to share, theinteraction with other individuals within the social network can expose, andeventually disclose, sensitive information. Among all the sharable personaldata, geo-location is particularly interesting. On one hand, users tend toconsider their current location as a very sensitive information, avoiding toshare it most of the time. On the other hand, service providers are interestedto extract and utilize geo-tagged data to offer tailored services. In thiswork, we consider the problem of inferring the current location of a userutilizing only the available information of other social contacts in the OSN.For this purpose, we employ a graph-based deep learning architecture to learn amodel between the users' known and unknown geo-location during a consideredperiod of time. As a study case, we consider Twitter, where the user generatedcontent (i.e., tweet) can embed user's current location. Our experimentsvalidate our approach and further confirm the concern related to data privacyin OSNs. Results show the presence of a critical-mass phenomenon, i.e., if atleast 10% of the users provide their tweets with geo-tags, then the privacy ofall the remaining users is seriously put at risk. In fact, our approach is ableto localize almost 50% of the tweets with an accuracy below 1km relying only ona small percentage of available information.

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