# Location reference identification from tweets during emergencies: A deep learning approach

arXiv: https://arxiv.org/abs/1901.08241 · HTML: https://mlsanity.com/paper/1901.08241/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 14 of 14)

- [Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN](https://mlsanity.com/paper/2408.06761/) (2024)
- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution](https://mlsanity.com/paper/2305.11315/) (2023)
- [Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey](https://mlsanity.com/paper/2209.07272/) (2022)
- [Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach](https://mlsanity.com/paper/2404.07399/) (2024)
- [Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input](https://mlsanity.com/paper/2404.18784/) (2024)
- [Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data](https://mlsanity.com/paper/2106.07114/) (2021)
- [Identification of Fine-Grained Location Mentions in Crisis Tweets](https://mlsanity.com/paper/2111.06334/) (2021)
- [Unsupervised Location Mapping for Narrative Corpora](https://mlsanity.com/paper/2504.05954/) (2025)
- [Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs](https://mlsanity.com/paper/2503.01386/) (2025)
- [Earthquake Response Analysis with AI](https://mlsanity.com/paper/2503.16509/) (2025)
- [Coordinates from Context: Using LLMs to Ground Complex Location References](https://mlsanity.com/paper/2510.08741/) (2025)
- [MFiSP: A Multimodal Fire Spread Prediction Framework](https://mlsanity.com/paper/2510.23934/) (2025)
- [Extracting Disaster Impacts and Impact Related Locations in Social Media Posts Using Large Language Models](https://mlsanity.com/paper/2511.21753/) (2025)

## Abstract (arXiv)

Twitter is recently being used during crises to communicate with officials and provide rescue and relief operation in real time. The geographical location information of the event, as well as users, are vitally important in such scenarios. The identification of geographic location is one of the challenging tasks as the location information fields, such as user location and place name of tweets are not reliable. The extraction of location information from tweet text is difficult as it contains a lot of non-standard English, grammatical errors, spelling mistakes, non-standard abbreviations, and so on. This research aims to extract location words used in the tweet using a Convolutional Neural Network (CNN) based model. We achieved the exact matching score of 0.929, Hamming loss of 0.002, and F1-score of 0.96 for the tweets related to the earthquake. Our model was able to extract even three- to four-word long location references which is also evident from the exact matching score of over 92%. The findings of this paper can help in early event localization, emergency situations, real-time road traffic management, localized advertisement, and in various location-based services.