Text-Based Twitter User Geolocation Prediction
نویسندگان
چکیده
منابع مشابه
Text-Based Twitter User Geolocation Prediction
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 r...
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We implement a city-level geolocation prediction system for Twitter users. The system infers a user’s location based on both tweet text and user-declared metadata using a stacking approach. We demonstrate that the stacking method substantially outperforms benchmark methods, achieving 49% accuracy on a benchmark dataset. We further evaluate our method on a recent crawl of Twitter data to investi...
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This paper describes the shared task for the English Twitter geolocation prediction associated with WNUT 2016. We discuss details of the task settings, data preparation and participant systems. The derived dataset and performance figures from each system provide baselines for future research in this realm.
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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) the incorporation of text-based geolocation priors for test users. Experiments over three Twitter benchmark datasets achieve state-of-the-art results, and demonstrate the effecti...
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We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is language independent, and despite minimal feature engineering, it is interpretable and capable of learning location indicative words and timing patterns. Compared to state-of-the...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2014
ISSN: 1076-9757
DOI: 10.1613/jair.4200