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Twitter User Geolocation Using a Unified Text and Network Prediction Model

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arxiv 1506.08259 v3 pith:XQU2IHPK submitted 2015-06-27 cs.CL cs.SI

Afshin Rahimi , Trevor Cohn , Timothy Baldwin This is my paper · ORCID
classification cs.CLcs.SI
keywords geolocationenhancementspredictiontwitterachieveadsorptionapproachbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal

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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) he incorporation of text-based geolocation priors for test users. Experiments over three Twitter benchmark datasets achieve state-of-the-art results, and demonstrate the effectiveness of the enhancements.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Interactive Global Geolocation Assistant

    cs.CV 2024-12 conditional novelty 6.0 of 10

    GaGA is an MLLM-based interactive geolocation system that improves country-level accuracy by 4.57% and city-level accuracy by 2.92% over OSV-5M-Baseline on a reproduced GWS15k benchmark.

  2. A Large-Scale Empirical Study of Geotagging Behavior on Twitter

    cs.SI 2019-08 conditional novelty 6.0 of 10

    A large-scale Twitter analysis shows geotagging is uneven across user groups, correlates with profile location reporting, and clusters in social networks, threatening assumptions behind geotagged-opinion research.

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