Fusing Text and Frienships for Location Inference in Online Social Networks

Location information is becoming prevalent in today´s online social networks (OSNs), which raises special privacy concerns with regard to both location sharing and its applications. Even when no explicit location is disclosed by a user, it is possible to geolocate the user through his/her social context, e.g., status updates and social relationships in OSNs. To demonstrate this, we propose GeoFind, which accurately identifies users´ geographic regions through effective fusion (re-ranking) of (1) text-based ranking using geo-sensitive textual features and (2) structure-based ranking using maximum likelihood estimation (MLE) of geotagged friends. Evaluation results using 0.8 million geotagged Twitter users over a 3-month period demonstrate that GeoFind outperforms state-of-the-art techniques, with significant reduction of estimation error (25% of average error, 66% of median error). The potential of improving location accuracy through the fusion of multiple data types calls for a re-examination of existing privacy protection policies and mechanisms.