Disaster-prevention Initiatives using AI and RelatedInformation on SNS
Kiyonori Ohtake

Executive Researcher
Application Laboratory Resilient ICT Research Center
National Institute of Information and Communications Technology

1. Introduction
Since the 2011 Tohoku Earthquake the value of social media
and SNS in particular, has been widely recognized. The abilityfor anyone to send information easily in real-time was shown tobe useful for disaster response, but it is also widely recognized thatsince SNS are quite anonymous, it is also easy to spread erroneous
information and cause confusion in society. Seeing the need to usesuch disaster-related information on SNS despite such qualities,
the National Institute of Information and Communication
Technology (NICT) began public trials in April 2015, of theDISAster-information ANAlyzer (DISAANA) system, whichanalyzes disaster SNS data on Twitter, and in October 2016, of
the Disaster-information SUMMarizer (D-SUMM) system,
which summarizes the state of disaster. These are being usedin disaster preparedness drills by local governments and in realdisasters, to verify the technology, and support further, ongoing
R&D. This article reviews these two systems, how they have been
used, and gives an overview of a system called “SOCial dynamics
Observation and victims support Dialogue Agent platform fordisaster management” (SOCDA) and related initiatives. SOCDA
is a chatbot for disaster management intended to resolve problems
identified in use of the earlier two systems and is the result ofR&D that began in 2018, as a second-term project of the Cabinet
Office’s Strategic Innovation Program (SIP).

2. Twitter analysis with DISAANA andD-SUMM
Simply put, DISAANA is a question answering system.
Question answering is a technology that selects answers toquestions from some body of knowledge (usually a large volume oftext such as an encyclopedia). Questions could be of various types:
Who, What, Where, When, Why, or How; but DISAANA
emphasizes understanding what happened and where in a disaster,so the focus is on “What” and “Where” questions. We have ahistory of R&D on such Q&A technology, and already provide
trial access to a Q&A system on the WISDOM X Web page(https://wisdom-nict.jp/). DISAANA can be considered asapplying some of the technology used for WISDOM X to Twitter,extending it to process place names appropriately. From thebeginning of R&D on DISAANA, we described the prototype
system to relevant staff at local governments and asked them about
difficulties in gathering information. They pointed out that eventhinking of questions and entering them can be difficult, and they
did not have time to carefully check all the Q&A results, so theyneeded a way to comprehend what disaster-related information

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was on the SNS, in a compact and user-friendly form. In parallelwith DISAANA R&D, there was also R&D to automaticallyextract disaster-related information found on SNS, and weaccelerated this work. Since 2014, we received support from theCabinet Office’s Strategic Innovation Project (SIP) for R&D
on D-SUMM, a system that automatically extracts disasterrelated information from SNS and from it, summarizes disasterconditions in a compact and user-friendly form. Trial operation ofthe system began in October, 2016. Details of these two systems
can be found in references at the end of this article.

3. Bi-directional communication on LINE withSOCDA
Twitter and other SNS enable anyone to post information
easily in real-time, immediately sharing conditions in far-away
places with photos and videos, so they hold great promise as
useful tools during disasters. On the other hand, a person postingdisaster information on Twitter and the like can post whatever
they want, so the information will not necessarily be useful indisaster response efforts. Furthermore, even if a person is able topost their distress situation on a SNS, and someone at a disasterresponse facility sees it and is able to provide information thatis useful for the original poster, human resources are limited intimes of disaster, and it would not be possible to respond to allsuch posts. This prompted the idea of developing an AI that could
autonomously gather such information and distribute information
appropriately, instead of a person. Starting in FY2018, we received
Cabinet Office second-term SIP support and began R&D
on the SOCDA chatbot for disaster management. R&D on
SOCDA is being conducted jointly with the National ResearchInstitute for Earth Science and Disaster Resilience (NIED) andWeathernews Inc. We also collaborated with LINE Corp. andthe Japan Institute of Law and Information Systems (JILIS) fortrials, operation and to study the system and other aspects ofimplementation in society.
SOCDA means Dialogue Agent platform for disaster management, and it is a platform for collecting and distributing disaster-related information through bi-directional communication,
mainly with text interaction on SNS. Broadly speaking, SOCDA
is not intended as a platform for a specific SNS, but currently weare collaborating with LINE Corporation to develop a chatbot
program operating on the LINE SNS, together with various
back-end programs, so for ordinary users accessing it from a LINE
account, SOCDA can be more narrowly defined as a chatbot program on LINE.

With over 80 million users, LINE is currently the SNS withthe most users in Japan, so in terms of coverage, it is promisingfor implementing a service in society. An overview of SOCDA
is given in Figure 1. There are two main functions implemented
by SOCDA. One is information gathering, and the other isinformation distribution. Rather than having individual citizens
providing information spontaneously and exhaustively, the desired
information can be collected, and processed, and new decisions
regarding disaster response can be distributed quickly. NICT
has contributed to SCODA R&D with technology to analyze
the collected information and visualize it on a map, applying
technologies from DISAANA and D-SUMM. In doing so,
we have advanced R&D on more user-friendly visualization
methods and more accurate methods for analyzing the collected
information.
In past R&D on DISAANA and D-SUMM, we haveshown that Twitter and other SNS are platforms that treat people
as a kind of sensor. However, by implementing bi-directional
communication on the SNS, extending the analogy, we can also
treat people as actuators. We could also say they are acting as
active sensors, rather than passive sensors. As such, we can expect

to be able to collect a range of information quickly, even underconditions that we could not previously, such as checking disasterconditions when a disaster has occurred during the night. Reliable
positioning information, photographs and video can also be sharednow, so it is possible to collect the desired information quickly, andinformation contributing to evacuation and other activities can bedistributed quickly.
When implementing bi-directional communication withthis sort of chatbot, it is desirable for the text chat to feel likeinteracting with a person, but when a large-scale disaster occurs,a real problem is that information needs to be collected from, anddistributed to, a huge number of people over an extremely shortperiod of time. As such, we prioritized scalability in the design
and implementation of SOCDA, so that we could maintain aminimum level of quality that people can tolerate in the disasterprevention-domain, while handling numbers of interactions in ashort time that would be overwhelming if attempted by humanpersonnel.
As a result of active trials with local governments and otherorganizations since FY2018, most issues are being clarified,including technical but also institutional and other issues. For■ Figure 1: Overview of SOCDA, a chatbot for disaster management

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example, there is a responsibility issue with a chatbot for disastermanagement. To enable bi-directional communication, members
must become friends with the SOCDA account, but without aclear commitment to taking responsibility in handling shared
information, it will be difficult to gain members’ cooperation.
There is also an issue of how the information provided to SOCDA
by residents is handled. In prior tests, information was gathered ina format that anyone could look at, and information was used ina way that the information provider remained anonymous. This
helped curb any resistance to providing information, but who
provided the information and from where was unknown, so it wasdifficult to encourage cooperation and information sharing within
the community.
On the other hand, relatively soon after R&D began, webegan studying use of SOCDA for communication with personnel
and collaborating facilities such as the fire department, rather thanwith general residents. Use by government or fire department
staff in this way means it is being used at the front lines ofdisaster response, and we realized there were hardware and otherconstraints that we had not anticipated earlier (Smartphones
require touch, so they cannot be used hands-free. We also want
to use speech recognition to simplify input, but could not always
obtain the desired results due to noise from heavy rain and othercauses).
We also heard from one government agency, that a large
amount of effort was required to confirm the safety of people
needing help with evacuation when a typhoon was approaching.
They inquired whether SOCDA could be used for that purpose.
We conducted two trials of this application. In the first trial, we
checked the basic functionality using test data, and in the second
trial, we had people needing help become friends on SOCDA andLINE, and enter their safety information under conditions similar
to a real environment. This showed the technical potential, butalso exposed issues requiring further study, such as how personal
information should be handled.
In January, 2020, we also conducted a large informationgathering drill using SOCDA on a scale of ten thousand
participants in Kobe City. We verified performance in collecting
a large volume of information in a short time, reduced the volumeand increased the speed when providing results of visualizing theinformation to many during that same short time. We were able
to verify and make improvements for even larger-scale use in thefuture.
We plan to continue implementing various planned features inSOCDA and to verify them in practical tests.

4. Disaster prevention and mitigation using
SNS in the Corona Era
From our experience with the spread of the new coronavirus,

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preventative measures are being taken in all aspects of our lives,
including the field of disaster prevention and mitigation. We haveexperienced large scale storm and flood damage every year forthe past several years, so large-scale disasters are sure to occur inthe future, and we are studying how to deal with them. Thereis a wide range of information currently circulating regarding
the novel coronavirus, and there may be those who have doubts
about an expression like “Corona Era,” but here, we use it here toindicate the current state in which we do not have immunity, andlive with the possibility that the disease could spread. This applies
more broadly than to the current novel coronavirus, and could also
apply, for example, to a new influenza virus.
There are two areas of difficulty in dealing with disasters inthis Corona Era, particularly related to evacuation efforts. The
first is that infected people will evacuate to shelters, producing socalled clusters and spreading the infection. The second is that anincreasing number of people are not seeking refuge appropriately
for fear of infection and this itself is a threat to their lives. We areworking to support disaster response by providing information tohelp with these issues. Interacting with the chatbot for disastermanagement can provide support by confirming disaster hazards,
checking people’s health status, and with decisions whether
evacuation is needed or not. Then, if there is suspected infection
when checking a person’s health before evacuation, SOCDA canautomatically provide information regarding suitable facilities thathave been prepared by the administration, rather than having aperson do it. For people that are considering evacuating, current
information regarding evacuation shelters can be provided in atimely manner to encourage appropriate evacuation measures, suchas whether they are crowded, or whether appropriate measures tohandle infection are being taken. Also, by presenting the optionof taking refuge in their own homes based on hazard informationsuch as where flooding is expected to occur, some will be able to
avoid the risks of evacuation shelters (enclosed spaces, crowdedconditions, close contact), and the risks of traveling to the shelter.
An example of a possible interaction with SOCDA for this sort ofapplication is shown in Figure 2.
We call evacuation to protect life, as discussed above, life
preserving evacuation, and we have discussed the potential foruse of SNS in this phase of evacuation. It is followed by a phase of
on-going life at the evacuation location, which we call “ongoingevacuation,” and there is also strong demand for SNS in thissecond phase. The discussion above regarding life preserving
evacuation envisioned mainly storm and flood damage, but thesituation is somewhat different for earthquakes. Whichever
the case, for large scale disasters in the Corona Era, we expectthat fully dispersed evacuation will need to be planned. Assuch, although in the past it was okay to provide the necessary
goods and services for ongoing evacuation, such as medical

■ Figure 2: Example of conversation on SOCDA during a disaster considering measures to prevent infection

help, centrally at designated evacuation shelters, in the Corona
Era, these must also be provided in a distributed fashion, oreach survivor will not be able to receive the support they need.
This applies not only to direct support, but is also importantfor information, and the SOCDA can be used to gather andprovide information comprehensively. This involves both (1)gathering information regarding evacuation locations (e.g. finding
non-designated or provisional shelter (hotels, etc.) locations)and whether medical treatment is needed, and (2) providing
information such as locations where supplies will be available, andproviding information necessary to preserve health in ongoing
evacuation (e.g. warnings regarding sleeping in a vehicle, etc.).
Most of these features were conceived when R&D on
the chatbot for disaster management began, but there was no
particular emphasis placed on using it to check the well-beingof survivors. In considering disaster response in the Corona Era,
this is a significant point of difference from earlier approaches.
While it is clear that there is a need to develop a chatbot that caninteractively check people’s well-being and provide appropriate
information in this way, in some cases this could involve handlingsensitive information, so various issues regarding operation andorganization will require further study, such as who will haveaccess to such collected information, and how will it be used. We
also expect that in practical terms, it will be important to havea mechanism to provide specific guidance to an online medical
service at an early stage. Discussions on these and other issues will
be conducted through the Council on Artificial Intelligence forDisaster Resilience (https://caidr.jp/), which was launched in June,
2019.

5. Conclusion
In this article, we have introduced initiatives using AI withdisaster-related information found on SNS, for disaster prevention
and mitigation. We first introduced two systems, DISAANA
and D-SUMM, which analyze disaster-related information on
Twitter. Then, we showed that information posted spontaneously
on Twitter is not adequately comprehensive, and we introduced
SOCDA, which is a chatbot for disaster management that resolves
the issue by implementing bi-directional communication on theLINE SNS. SOCDA is still under R&D, and we will continue
working to contribute to disaster prevention and mitigation,
while also incorporating features to prevent the spread of the newcoronavirus.
References
J. Mizuno, M. Tanaka, K. Ohtake, J.H. Oh, J. Kloetzer, C. Hashimoto and K. Troisawa:“WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing Textual
Big Data,” Proceedings of the 26 th International Conference on Computational Linguistics(COLING 2016), December, 2016.

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