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The geolocation of online information is an essential component in anygeospatial application. While most of the previous work on geolocation hasfocused on Twitter, in this paper we quantify and compare the performance oftext-based geolocation methods on social media data drawn from both Blogger andTwitter. We introduce a novel set of location specific features that are bothhighly informative and easily interpretable, and show that we can achieve errorrate reductions of up to 12.5% with respect to the best previously proposedgeolocation features. We also show that despite posting longer text, Bloggerusers are significantly harder to geolocate than Twitter users. Additionally,we investigate the effect of training and testing on different media(cross-media predictions), or combining multiple social media sources(multi-media predictions). Finally, we explore the geolocability of socialmedia in relation to three user dimensions: state, gender, and industry.

A Comparative Analysis of Content-based Geolocation in Blogs and Tweets