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Location Mention Recognition from Japanese Disaster-Related Tweets

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Information Technology in Disaster Risk Reduction (ITDRR 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 672))

Abstract

In order to minimize the damage inflicted by large-scale disasters, it is essential to collect and disseminate information quickly and accurately. In recent years, various national agencies and local municipalities have used Twitter and other highly immediate social media to help focus their disaster relief efforts. Because the volume of information circulating on social media increases rapidly during a disaster, the ability to quickly sort out valuable posts from the massive volume of posts that appear is essential. In the case of Twitter, it is vital for early responders to identify the location of relevant tweets in order to facilitate decision making and focus their response. To help in this task, attempts have been made to use machine learning to classify genres, extract useful information, and identify locations and points of interest for groups of tweets posted during a disaster. However, since preparing training data and building a model during the early stages of a disaster are extremely challenging, using a model built on past disaster tweet data offers a promising possibility. In this study, we focus on three heavy rain disasters that occurred in Japan and examine the extraction of the location mentions in tweets using models learned from tweets posted during prior disasters.

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Acknowledgments

This research was supported by JSPS KAKENHI Grant Number 18K11553.

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Authors and Affiliations

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

    Toshihiro Rokuse & Osamu Uchida

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  1. Toshihiro Rokuse
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  2. Osamu Uchida
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Correspondence to Toshihiro Rokuse or Osamu Uchida .

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Editors and Affiliations

  1. University of Agder, Kristiansand, Norway

    Terje Gjøsæter

  2. University of Agder, Kristiansand, Norway

    Jaziar Radianti

  3. Tsuda University, Tokyo, Japan

    Yuko Murayama

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Rokuse, T., Uchida, O. (2023). Location Mention Recognition from Japanese Disaster-Related Tweets. In: Gjøsæter, T., Radianti, J., Murayama, Y. (eds) Information Technology in Disaster Risk Reduction. ITDRR 2022. IFIP Advances in Information and Communication Technology, vol 672. Springer, Cham. https://doi.org/10.1007/978-3-031-34207-3_19

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  • DOI: https://doi.org/10.1007/978-3-031-34207-3_19

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