Geo-Location Identification of Facebook Pages

Online Social Network (OSN) communities serve as different platforms for multiple users' interaction - people behaving diversely among distinctive communities - such as entertainment, global and local discussion communities. However, attribute identification among online discussion communities remain largely unexplored. In this paper, we describe and analyze the geo-location property of large-scale Facebook public pages (15M pages). We propose a framework utilizing the connectivity of the page-like graph to predict the missing geo-location information based on Breadth-First Search (BFS). Our method achieves a satisfyingly high accuracy (89 %) on identifying the state location attribute of unknown United States (US) pages. Our empirical results offer a better understanding of regional social analysis and target audience broadcasting.