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• W2142191319 abstract "Geographical location is vital to geospatial applications like local search and event detection. In this paper, we investigate and improve on the task of text-based geolocation prediction of Twitter users. Previous studies on this topic have typically assumed that geographical references (e.g., gazetteer terms, dialectal words) in a text are indicative of its author’s location. However, these references are often buried in informal, ungrammatical, and multilingual data, and are therefore non-trivial to identify and exploit. We present an integrated geolocation prediction framework and investigate what factors impact on prediction accuracy. First, we evaluate a range of feature selection methods to obtain “location indicative words”. We then evaluate the impact of non-geotagged tweets, language, and user-declared metadata on geolocation prediction. In addition, we evaluate the impact of temporal variance on model generalisation, and discuss how users differ in terms of their geolocatability. We achieve state-of-the-art results for the text-based Twitter user geolocation task, and also provide the most extensive exploration of the task to date. Our findings provide valuable insights into the design of robust, practical text-based geolocation prediction systems." @default.
• W2142191319 created "2016-06-24" @default.
• W2142191319 creator A5037912029 @default.
• W2142191319 creator A5047240103 @default.
• W2142191319 creator A5060332228 @default.
• W2142191319 date "2014-03-20" @default.
• W2142191319 modified "2023-10-18" @default.
• W2142191319 title "Text-Based Twitter User Geolocation Prediction" @default.
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• W2142191319 doi "https://doi.org/10.1613/jair.4200" @default.
• W2142191319 hasPublicationYear "2014" @default.
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