“Current City ” Prediction for Coarse Location Based Applications on Facebook

Abstract—Location-Based services with social networks im-prove users ’ experience and enrich people’s social live. However, location information is often inadequate due to privacy and security concerns. We seek to infer users ’ ‘Current City ’ on Facebook for coarse location based applications. We first extract users ’ multiple explicit and implicit location attributes, and analyze correlations of these attributes from two perspective: user-centric and user-friends. We observe that both user-centric and user-friends location attributes tightly correlate to a user’s Current City (e.g., 60 % of users stay in their hometown, 60% of users live in the same city as 50 % of their friends). Based on extensive analysis and observations on location attributes correlations, we have constructed a Current City Prediction model (CCP) using artificial neural network (ANN) learning frameworks. The experimental results indicate that we achieve accuracy levels of 84 % for city-level prediction and 98 % for country-level which are increases of 9 % and 18%, respectively than what is possible with Tweecalization. Keywords—LBA; Coarse Location; Location Prediction; I