Inference models for Twitter user's home location prediction

Abstract

Twitter has emerged as one of the most powerful micro-blogging services for real-time sharing of information on the web. A large base of Twitter users tend to post short messages of 140 characters (Tweets) reflecting a variety of topics. Location-based-services (LBSs) may be built on top of microblogs to provide for targeted advertisement, news recommendation, or even microblogs personalization. Knowing the user's home location would empower such LBSs. In this paper, we propose prediction models to infer the users' home location based on their social graph and tweets content. The problem is non trivial as the tweets are short and not many people like to share their location for privacy concerns. Our extensive performance evaluation on a publicly available dataset demonstrates the effectiveness of the proposed models. The proposed models outperform the competitive state-of-the-art home location inference techniques that are based on the social graph, tweet content, and both by a relative gain in the F-measure of up to 37.71%, 29%, and 9.06%, respectively.