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

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.

4 pages, 2 tables, 1 figure, SIGSPATIAL GeoAI Workshop

References in corpus (2)

• Multiview Deep Learning for Predicting Twitter Users' Location
• End-to-end Network for Twitter Geolocation Prediction and Hashing