Analysis of Rescue Request and Damage Report Tweets Posted during 2019 Typhoon Hagibis

Utsu, Keisuke; Uchida, Osamu

doi:10.1587/transfun.2020eal2040

Cited by 5 publications

(1 citation statement)

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“…In such situations, SNS information updates can help increase survival possibilities because they often include real-time data collected from on-site users in the disaster areas, news on help being provided by government agencies, or other knowledge that should be widely publicized [2]. In this regard, with more than 45 million active users, Twitter is the most widely utilized SNS in Japan.…”

Section: Introductionmentioning

confidence: 99%

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

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Natural disasters are events that humans cannot control, and Japan has suffered from many such disasters over its long history. Many of these have caused severe damage to human lives and property. These days, numerous Japanese people have gained considerable experience preparing for disasters and are now striving to predict the effects of disasters using social network services (SNSs) to exchange information in real time. Currently, Twitter is the most popular and powerful SNS tool used for disaster response in Japan because it allows users to exchange and disseminate information quickly. However, since almost all of the Japanese-related content is also written in the Japanese language, which restricts most of its benefits to Japanese people, we feel that it is necessary to create a disaster response system that would help people who do not understand Japanese. Accordingly, this paper presents the framework of a question-answering (QA) system that was developed using a Twitter dataset containing more than nine million tweets compiled during the Osaka North Earthquake that occurred on 18 June 2018. We also studied the structure of the questions posed and developed methods for classifying them into particular categories in order to find answers from the dataset using an ontology, word similarity, keyword frequency, and natural language processing. The experimental results presented herein confirm the accuracy of the answer results generated from our proposed system.

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Section: Introductionmentioning

confidence: 99%