Abstract
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements: (1) the removal of "celebrity" nodes to increase location homophily and boost tractability; and (2) the incorporation of text-based geolocation priors for test users. Experiments over three Twitter benchmark datasets achieve state-of-theart results, and demonstrate the effectiveness of the enhancements.
Cite
CITATION STYLE
Rahimi, A., Cohn, T., & Baldwin, T. (2015). Twitter user geolocation using a unified text and network prediction model. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 2, pp. 630–636). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-2104
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