# SAVITR: A System for Real-time Location Extraction from Microblogs during Emergencies

arXiv: https://arxiv.org/abs/1801.07757 · HTML: https://mlsanity.com/paper/1801.07757/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2018.

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

- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [Where did you tweet from? Inferring the origin locations of tweets based on contextual information](https://mlsanity.com/paper/2211.16506/) (2022)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [Extracting Disaster Impacts and Impact Related Locations in Social Media Posts Using Large Language Models](https://mlsanity.com/paper/2511.21753/) (2025)

## Abstract (arXiv)

We present SAVITR, a system that leverages the information posted on the Twitter microblogging site to monitor and analyse emergency situations. Given that only a very small percentage of microblogs are geo-tagged, it is essential for such a system to extract locations from the text of the microblogs. We employ natural language processing techniques to infer the locations mentioned in the microblog text, in an unsupervised fashion and display it on a map-based interface. The system is designed for efficient performance, achieving an F-score of 0.79, and is approximately two orders of magnitude faster than other available tools for location extraction.