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Location Extraction in Disaster Tweets with a Model Trained on Past Data: Diverse Analysis

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Information Technology in Disaster Risk Reduction (ITDRR 2023 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 706))

Abstract

Rapid and accurate collection and dissemination of information is essential to minimize damage in a large-scale disaster. Governments and local authorities responsible for these tasks actively use immediate platforms like Twitter (now X) to gather and share information. However, the volume of information on social media increases rapidly during a large-scale disaster. It becomes necessary to swiftly select crucial, urgent information from many tweets. Identifying the locations mentioned in these tweets is also essential to facilitate decision-making by disaster responders. Considering these perspectives, quickly and manually sorting through the massive volume of posts is not easy, and attempts are being made to employ machine learning models for the sorting process. However, disaster response requires a rapid reaction, while machine learning models need high-quality training data to perform effectively. This study considers using posts circulated during past disasters to resolve these conflicting issues. A research question addressed is whether the type of disaster affects the accuracy of extracting location mentions using data from past disasters. This paper reports the verification results for the same and different disaster types.

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Acknowledgments

This research was supported by JSPS KAKENHI Grant Number 22K12277.

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Authors and Affiliations

  1. Department of Information Media Technology, Tokai University, Hiratsuka, Kanagawa, Japan

    Toshihiro Rokuse, Keisuke Utsu & Osamu Uchida

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  1. Toshihiro Rokuse
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  2. Keisuke Utsu
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  3. Osamu Uchida
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Correspondence to Toshihiro Rokuse .

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  1. University Grenoble Alps, Saint-Martin-d’Hères, France

    Julie Dugdale

  2. University of Agder, Kristiansand, Norway

    Terje Gjøsæter

  3. Tokai University, Hiratsuka, Japan

    Osamu Uchida

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Rokuse, T., Utsu, K., Uchida, O. (2024). Location Extraction in Disaster Tweets with a Model Trained on Past Data: Diverse Analysis. In: Dugdale, J., Gjøsæter, T., Uchida, O. (eds) Information Technology in Disaster Risk Reduction. ITDRR 2023 2023. IFIP Advances in Information and Communication Technology, vol 706. Springer, Cham. https://doi.org/10.1007/978-3-031-64037-7_9

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