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arXiv · 1910.02213

City-level Geolocation of Tweets for Real-time Visual Analytics

Luke S. Snyder·Morteza Karimzadeh·Ray Chen·David S. Ebert
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Abstract

Real-time tweets can provide useful information on evolving events and situations. Geotagged tweets are especially useful, as they indicate the location of origin and provide geographic context. However, only a small portion of tweets are geotagged, limiting their use for situational awareness. In this paper, we adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness. We provide computational evaluations to demonstrate the superiority and utility of our geolocation prediction model within an interactive system.

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Luke S. Snyder, Morteza Karimzadeh, Ray Chen, David S. Ebert. 2019-10-05. City-level Geolocation of Tweets for Real-time Visual Analytics. https://doi.org/10.1145/3356471.3365243

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