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Users' locations are important for many applications such as personalizedsearch and localized content delivery. In this paper, we study the problem ofprofiling Twitter users' locations with their following network and tweets. Wepropose a multiple location profiling model (MLP), which has three keyfeatures: 1) it formally models how likely a user follows another user giventheir locations and how likely a user tweets a venue given his location, 2) itfundamentally captures that a user has multiple locations and his followingrelationships and tweeted venues can be related to any of his locations, andsome of them are even noisy, and 3) it novelly utilizes the home locations ofsome users as partial supervision. As a result, MLP not only discovers users'locations accurately and completely, but also "explains" each followingrelationship by revealing users' true locations in the relationship.Experiments on a large-scale data set demonstrate those advantages.Particularly, 1) for predicting users' home locations, MLP successfully places62% users and outperforms two state-of-the-art methods by 10% in accuracy, 2)for discovering users' multiple locations, MLP improves the baseline methods by14% in recall, and 3) for explaining following relationships, MLP achieves 57%accuracy.

Multiple Location Profiling for Users and Relationships from Social Network and Content