Yumi Yoshitsugu

Abstract: Traditional means for contacting emergency responders depend critically on the availability of the 911 service to request help. Large-scale natural disasters such as hurricanes and earthquakes often result in overloading and sometimes failure of communication facilities. Affected citizens are increasingly using social media to obtain and disseminate information. Social media is not only being used to communicate with first responders but also for people to organically volunteer and seek help from each other, complementing the role of first responders. In this paper, we examine the use of Twitter during two major hurricanes in the U.S. in 2017. We find that there exists a sizable number of people with access to the Internet even in areas where 911 services were down, and they tweet disaster-related information including requests for help. Our analysis indicates that social media can potentially help in disaster management and improve outcomes.

Abstract: Preserving social Web datasets is a crucial part of research work for disaster management based on information from social media. This paper describes the Michinoku Shinrokuden disaster archive project, mainly dedicated to archiving data from the 2011 Great East Japan Earthquake and its aftermath. Social websites should of course be part of this archive. We discuss issues in archiving social websites for the disaster management research communities and introduce our vision for Michinoku Shinrokuden.

Abstract: The recent earthquake in Japan showed that tourists cannot access evacuation information and the families of tourists experienced problems when accessing safety information related to tourists. Given these problems, we consider two issues related to information provision in disaster situations. The first issue is the lack of evacuation information for tourists. The second issue is the difficulty of confirming the safety of tourists and sharing their safety information with relevant people, including the tourist’s family. The present study focuses on developing a tourism information system to solve these issues. We refer to this system as an Educational Trip Support System (ETSS). The research subject is a school trip, which is a representative type of group tour that occurs in Japan. The objectives of the ETSS are to help students to escape to an evacuation area rapidly by providing evacuation information and to share safety confirmations with relevant people during disaster situations. We assessed the effectiveness based on a field test in a disaster-simulated situation and quantitative surveys. The major contributions of this study include (1) a description of a mobile application system for confirming safety during school trips and sharing information with relevant people, (2) a method to facilitate the rapid evacuation of students that saves time and reduces their concerns about the situation, (3) detailed evaluations of the performance obtained using ETSS.

Abstract: The present paper aims to apply a new neural learning method called "Neural Potential Learning, NPL" to the classification and interpretation of tweets. It has been well known that social media such as the Twitter play crucial roles in transmitting important information at the time of natural disasters. In particular, since the Great East Japan Earthquake in 2011, the Twitter has been considered as one of the most efficient and convenient communication tools. However, because much redundant information is contained in the tweets, it is usually difficult to obtain important information from the flows of the tweets. Thus, it is urgently needed to develop some methods to extract the important and useful information from redundant tweets. To cope with complex and redundant data, a new neural potential learning has been developed to extract the important information. The method aims to find some highly potential neurons and enhance those neurons as much as possible to reduce redundant information and to focus on important information. The method was applied to the real tweets data collected in the earthquake and it was found that the method could classify the tweets as important and unimportant ones more accurately than the other conventional machine learning methods. In addition, the method made it possible to interpret how the tweets could be classified, based on the examination of highly potential neurons.

Abstract: In this paper, we outline our analysis of mass media and social media as used for disaster management. We looked at the differences among multiple sub-corpuses to find relatively unique keywords based on chronologies, geographic locations, or media types. We are currently analyzing a massive corpus collected from Internet news sources and Twitter after the Great East Japan Earthquake.