@inproceedings{0ee35216fdc445b29a5041a0faff47b9,
title = "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.",
keywords = "Deep learning, Geolocation prediction, Twitter",
author = "Snyder, \{Luke S.\} and Morteza Karimzadeh and Ray Chen and Ebert, \{David S.\}",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright is held by the owner/author(s). Publication rights licensed to ACM.; 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019 ; Conference date: 05-11-2019",
year = "2019",
month = nov,
day = "5",
doi = "10.1145/3356471.3365243",
language = "English (US)",
series = "Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019",
publisher = "Association for Computing Machinery",
pages = "85--88",
editor = "Song Gao and Shawn Newsam and Liang Zhao and Dalton Lunga and Yingjie Hu and Bruno Martins and Xun Zhou and Feng Chen",
booktitle = "Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019",
}