Abstract

We describe a method that predicts the location of user-generated content using textual features alone. Unlike previous methods for geotagging text documents, our proposed method is not sensitive to how we discretize space. We also discover that spatial resolution has an impact on the prediction accuracy, which allows us to trade-off the spatial resolution of the predicted location against our confidence about its accuracy. Our method can be used to estimate the error in document's predicted location, enabling us to filter out poor quality predictions. We evaluate the proposed method extensively on user-generated content collected from two different social media sites, Flickr and Twitter. Our evaluation examines its performance on the geotagging task and with respect to different parameters. We achieve state-of-the-art results for all three tasks: location prediction, error estimation and result ranking and also provide …

Date

November 4, 2014

Authors

Suradej Intagorn, Kristina Lerman

Book

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages

413-416