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

arXiv: https://arxiv.org/abs/1910.02213 · HTML: https://mlsanity.com/paper/1910.02213/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [ContCommRTD: A Distributed Content-based Misinformation-aware Community Detection System for Real-Time Disaster Reporting](https://mlsanity.com/paper/2301.12984/) (2023)
- [Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data](https://mlsanity.com/paper/2106.07114/) (2021)
- [Fine-grained Geolocation Prediction of Tweets with Human Machine Collaboration](https://mlsanity.com/paper/2106.13411/) (2021)
- [GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Research](https://mlsanity.com/paper/2210.13207/) (2022)

## Abstract (arXiv)

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.