Ariana S. Minot, Andrew Heier, Davis E. King, O. Simek, N. Stanisha
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The microblogging service Twitter is an increasingly popular platform for sharing information worldwide. This motivates the potential to mine information from Twitter, which can serve as a valuable resource for applications such as event localization and location-specific recommendation systems. Geolocation of Twitter messages is integral to such applications. However, only a a small percentage of Twitter posts are accompanied by a GPS location. Recent works have begun exploring ways to estimate the unknown location of Twitter users based on the content of their posts and various available metadata. This presents interesting challenges for natural language processing and multi-objective optimization. We propose a new method for estimating the home location of users based on both the content of their posts and their social connections on Twitter. Our method achieves an accuracy of 77% within 10 km in exchange for a reduction in coverage of 76% with respect to techniques which only use social connections.
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