A Real-Time Information Sharing System to Support Self-, Mutual-, and Public-Help in the Aftermath of a Disaster Utilizing Twitter

Uchida, Osamu; Kosugi, Masanori; Endo, Gaku; Funayama, Takamitsu; Utsu, Keisuke; Tajima, Sachi; Tomita, Makoto; Kajita, Yoshitaka; Yamamoto, Yoshiro

doi:10.1587/transfun.e99.a.1551

Cited by 30 publications

(8 citation statements)

References 6 publications

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“…Lots of methods for the analysis of social media to create a disaster victims helping system. Several of them are explained in the following: Disaster information tweeting system (DITS) and Disaster information mapping system (DIMS) is the application to share disaster information and help the user when disaster happens by use geolocation information and hashtag [4]. This system implemented as a web-based application.…”

Section: Related Workmentioning

confidence: 99%

Classification of Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

IFIP Advances in Information and Communication Technology

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Nowadays, social media is one of the essential sharing of information and proliferation tools because it spreads text messages, news, pictures, or videos in real-time. During the disaster, Japanese people use social media to exchange real-time information for their social interaction. Twitter is the most popular tool that has been used for disaster response in Japan. Even though many disaster systems have been created and used for disaster mitigation in Japan, most of them are assumed to be used by the Japanese in the Japanese language. From this problem, this study focuses on the way to create a disaster response system and community service to help, collect, and extract information on social media to help disaster mitigation becomes more important. This paper aims to investigate the tweets by focusing on noun keywords during the Osaka North Earthquake on 18 June 2018 with a data set of more than 9,000,000 tweets. The process presented classify social media messages by using ontology, word similarity, frequency of keyword, and evaluate results of natural language processing. We organize the messages into 15 categories and used as the classification algorithms with machine learning features of the count of each category word in the sentences. The result tweets were statistically compared with the keyword in each category to classify the content and collecting disaster information and using the result to build the analysis system.

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Section: Related Workmentioning

confidence: 99%

Classification of Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

IFIP Advances in Information and Communication Technology

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“…The integration of social media data and GIS applications has become important for disaster prevention and mitigation. Uchida et al proposed a web application called the disaster information tweeting and mapping system (DITS/DIMS) [41,42]. In this system, smartphone users can easily send their disaster-related information through Twitter, and the registered information is mapped on an online map.…”

Section: Facilitation Of Sharing Disaster-related Informationmentioning

confidence: 99%

Twitter-Based Safety Confirmation System for Disaster Situations

et al. 2020

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In the aftermath of disastrous events in Japan, safety information and rescue requests, as well as emergency alerts and damage situations, have been shared on Twitter. However, even victims who are familiar with smartphones or similar devices and social media cannot easily share detailed information, such as the coordinates or address of their current location, which are essential components of safety information and rescue requests. Moreover, local governments and rescue experts have difficulty in gathering such tweets from Twitter. In this paper, we propose a novel system to enable the victims to share their safety information, make rescue requests, and enable quick information gathering for decision making by local government staff or rescue experts. The proposed system is a Twitter-based safety confirmation system named T-@npi. Using the proposed application, the users can easily submit their safety information and send rescue requests on Twitter. The users who want to confirm the safety information can check it quickly on Twitter or via this system. Furthermore, the registered safety information is displayed on an online map to support rescue and assistance activities by local governments and rescue experts.

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“…For example, the Disaster Information Tweeting System (DITS) and the Disaster Information Mapping System (DIMS) are currently among the most popular mobile web-based applications used in Japan. These applications use a Twitter account to link with geolocation data and hashtags to obtain and share disaster information among participating users [14].…”

Section: Related Workmentioning

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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A Real-Time Information Sharing System to Support Self-, Mutual-, and Public-Help in the Aftermath of a Disaster Utilizing Twitter

Cited by 30 publications

References 6 publications

Classification of Social Media Messages Posted at the Time of Disaster

Classification of Social Media Messages Posted at the Time of Disaster

Twitter-Based Safety Confirmation System for Disaster Situations

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

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