City-level Geolocation of Tweets for Real-time Visual Analytics
This work addresses the need for better geographic context in real-time tweet analysis for situational awareness, but it is incremental as it builds on an existing state-of-the-art model.
The paper tackled the problem of limited geotagged tweets for real-time situational awareness by adapting and improving a deep learning model for city-level geolocation prediction, integrating it into a visual analytics system and demonstrating its superiority through computational evaluations.
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.