Shibuya et al

Detecting Disaster Recovery Activities via Social Media
Communication Topics

Detecting Disaster Recovery Activities
via Social Media Communication
TopicsYuya Shibuya

The University of Tokyo
The Graduate School of Interdisciplinary
Information Studies
shibuysyuya@gmail.com

Hideyuki Tanaka

The University of Tokyo
The Graduate School of Interdisciplinary
Information Studies
tanaka@iii.u-tokyo.ac.jp

ABSTRACT

Enhancing situational awareness by mining social media has been widely studied, but little work has been done
focusing on recovery phases. To provide evidence to support the possibility of harnessing social media as a sensor
of recovery activities, we examine the correlations between topic frequencies on Twitter and people’s socioeconomic recovery activities as reflected in the excess demand for used cars and housing, after the Great East
Japan Earthquake and Tsunami of 2011. Our research suggests that people in the disaster-stricken area
communicated more about recovery and disaster damages when they needed to purchase used cars, while the nonlocal population communicated more about going to and supporting the disaster-stricken area. On the other hand,regarding the excess demand for housing, when the local population of the disaster-stricken area started to resettle,
they communicated their opinions more than in other periods about disaster-related situations.
Keywords

Social Media, Topic modeling, Socio-economic recovery, Used-car demand, Housing demand.
INTRODUCTION

There have been various efforts to use social media data to detect the situation surrounding disaster-stricken
communities. However, few studies focus on detecting recovery. This paper explores the possibilities of detecting
socio-economic recovery activities by exploring social media communication topics and market data. To build
resilient communities after a large-scale disaster, improving mid-to-long-term situational awareness could
enhance recovery efforts. This is because traditional socio-economic recovery indicators (e.g., observing changes
in population and consumption, and conducting questionnaire surveys) are not published in a real-time way. Social
media data hold the possibility of filling this gap by its timeliness. This study conducts topic analysis on Twittercommunication data for the first six months after the Great East Japan Earthquake and Tsunami of 2011, and looks
into the relationships between the topic appearance ratios and two types of socio-economic activities, namely,
excess demand for used cars and excess demand for housing. The Great East Japan Earthquake and Tsunami
occurred on March 11th, 2011. It caused devastating building damage across the coastal areas. According to theJapanese Fire and Disaster Management Agency1, 19,667 people were killed, and 2,566 people went missing. The
disaster destroyed 121,787 buildings.
This study takes these two goods as proxies of people’s socio-economic recovery activities to rebuild their dailylives because related research and reports have demonstrated that both used cars and housing are in higher demandin affected communities when a large-scale water-related disaster hits (the details are explained later). According
to Drabek (1986), a disaster involves four phases: (1) preparedness, (2) response, (3) recovery, and (4) mitigation.
This study focuses on the recovery phase. We particularly analyze the data collected during the first six months
after the disaster.
The rest of the paper is constructed as follows. First, we review related literature and introduce research questions.
Secondly, the data are described. Then we conduct topic analysis on social media communication data and topic
frequency scores are calculated. To analyze the possibility of using social media data as a sensor of recovery
activities, we analyze the correlations between the topic frequency ratios and the proxies of socio-economic
1 http://www.fdma.go.jp/bn/higaihou_new.html

CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
Zeno Franco, José J. González and José H. Canós, eds.

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Detecting Disaster Recovery Activities via Social Media
Communication Topics

recovery activities. Lastly, we explain and discuss the results, and conclude with implications for future study.
RELATED LITERATURE
Socio-economic Recovery Activities: The Excess Demand for Used Cars and Housing

When a large-scale water-related disaster (e.g., hurricane, typhoon, tsunami) hits a community, there tends to be
an excess demand for used cars and for housing in the local community. For example, after Hurricane Harvey andIrma in 2017, the media reported excess demand for used cars around the flooded areas (Breuninger, 2017; Chee,
2017; Lang, 2017). Likewise, after the Great East Japan Earthquake and Tsunami, newspapers reported a rise in
the demand for used cars in the disaster-stricken area to replace water-damaged vehicles. Used cars were preferredover new cars because of their lower prices and quicker registration process (e.g., Nikkei Sangyo Shinbun, 2011,
The Tohoku Finance Bureaus, 2017). In addition, Shibuya and Tanaka (2018a) conducted interviews with usedcar dealers in disaster-stricken areas and revealed that the affected population needed to buy used cars when they
became active in restarting their daily-lives (e.g., restarting working).
Regarding housing, various reports described the excess demand for non-inundated housing after a large-scaledisaster. For instance, after Hurricane Sandy which flooded more than 300,000 homes and approximately 23,400
businesses (Bloomberg, 2013, p.13), the media reported increased demand for dry houses near the damaged areas(Kearns, Park and Buhayar, 2012; NBC, 2014; Racioppi, 2014). Another example of increased demand for
housing occurred after the Great East Japan Earthquake and Tsunami, which destroyed 121,783 buildings acrossthe coastal areas2. The newspapers reported that although the land values declined in the inundation zones, lands
located on hills were in higher demand, causing increased land prices (Asahi Shimbun, 2011).
Both used-car and housing market data have been widely analyzed based on the Hedonic model (e.g., Barr et al.,2017; Haan and Boer, 2010; Hallstrom and Smith, 2005; Kooreman and Haan, 2006; McCoy and Zhao, 2018;
McKen-zie and Levendis, 2010). The Hedonic model is an economic model that postulates that the price of a
product reflects a bundle of embodied characteristics valued by some implicit or shadow prices (Prieto et al.,2015). With the Hedonic model, we can examine used-car/housing prices, which reflect the demand controlled by
used-car/housing characteristics. For example, if there is excess demand for housing in a particular area, the pricesof housing in the area may rise to re-equilibrate the excess demand. Also, if the amount of supply of goods is thesame or more than those of corresponding periods of the previous year, the rises in prices could be explained by
the excess demand. Therefore, although analyses focus on prices, the changes in prices controlled by the usedcars/housing characteristics also reflects excess demand for used cars/housing. The previous studies examined theused-car/housing market data by applying the Hedonic model and clarified the excess demand for usedcars/housing after large-scale disasters. More specifically, Shibuya and Tanaka (2019) statistically showed that
there was an excess demand for used cars, particularly for Light Motor Vehicles3 in the disaster-stricken area after
the Great East Japan Earthquake and Tsunami. Also, Shibuya and Tanaka (2018b) analyzed housing market dataof New York City and found there was an excess demand for dry housing in New York City after Hurricane
Sandy. Another empirical study (Shibuya and Tanaka, 2018c) analyzed the leased housing market data after theGreat East Japan Earthquake and Tsunami, and found there was an excess demand for dry houses located within
3km to the building damage zones. These excess demands for both used cars and housing after the large-scaledisasters can be used as proxies of one of the socio-economic disaster recovery activities because people in thedisaster area needed these goods and were willing to pay to purchase them. Furthermore, as to used cars, theinterviews with used-car dealers showed that people purchased used cars when they started to be active inrebuilding their daily lives, resuming their work and shopping (Shibuya and Tanaka, 2018a). Regarding housing,people from the disaster areas seeking new housing means that people moved their physical locations, for example,
from an evacuation shelter to an apartment, and thus, were active in restarting their lives or moving forward to thenext stage of recovery. Therefore, in this study, we use used-car and housing market data as proxies of socioeconomic recovery activities in the case of the Great East Japan Earthquake and Tsunami.
Social Media for Improving Disaster Situational Awareness

When a disaster hits a community, a massive amount of information that includes individual discussions, andpersonal experiences and thoughts, is created. Because acquiring useful information about the situations in thedisaster-stricken area is critical for relief operations and preventing further losses, social media users have beenrecognized as “people as sensors” (Laituri and Kodrich, 2008), “citizen sensing” (Castillo, 2016; Sheth, 2009), or“ human sensors” (Yuan et al., 2013). Various related works have looked into the potential usefulness of social
media as “people as sensors” by investigating the correlations between cyber and real-world activities. For
instance, the relationships between chatter on Twitter and donation amounts (Korolov et al., 2015), the proximity
2 http://www.fdma.go.jp/bn/higaihou_new.html(accessed September 30, 2018, in Japanese)

3 A Japanese car category whose engine volumes are 660cc or less.

CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
Zeno Franco, José J. González and José H. Canós, eds.

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Detecting Disaster Recovery Activities via Social Media
Communication Topics

of a hurricane path and social media activities (Kryvasheyeu et al., 2016), and sentiment on social media andactual disaster damage (Guan and Chen, 2014; Kelly and Ahmad, 2014; Nguyen et al., 2014) have been addressed.
Also, other researchers argue that social media can be used as earthquake detectors (Kropivnitskaya et al., 2017;
Sakaki et al., 2013, 2010), water level sensors (de Albuquerque et al., 2015; Zhang et al., 2016), and sensors ofevacuation activities (Kibanov et al., 2017; Martin et al., 2017; Wang et al., 2016; Yuan et al., 2013). These studies
suggest that social media data can promote “people as sensor” approaches for disaster management by providing
effective and near real-time complement data for detecting or assessing disaster-related incidents (Martin et al.,2017). However, little work has been done on adapting the “people as sensors” approach in recovery. Some ofthe examples of studies focusing on social media use in recovery are Su et al (2017) and Yan et al. (2017). Yan etal. (2017) analyzed geotagged Flickr (a photo-based social network) photos after the 2013 Earthquake andTyphoon in the Philippines (ranging from April 2004 to July 2016) to identify remaining damage by mining tourist
photos. Su et al. (2017) investigated the social media communication patterns after the Great East Japan
Earthquake and Tsunami by focusing mainly on nueclear-related topics. In terms of the recovery phase, social
media data have the potential to complement traditional socio-economic recovery indicators (e.g., changes in
population and consumption, and conducting questionnaire surveys) because such data can provide fine-grained
measurements of behavior over time while taking advantage of significant population sample sizes (Gruebner etal., 2017). Detecting recovery activities in a near real-time way may help recovery-related agencies, local public
officials and recovery practitioners to obtain a big picture. Furthermore, improving the affected areas’ real-timesituational awareness may help create an evidence-based and more effective recovery plan.
Local and Non-local to a DisasterIn terms of social media communication data after a large-scale disaster, existing studies have recognized different
communication patterns between locals and non-locals during disasters. Starbird et al. (2012) argue that social
media contents shared by local citizens in a disaster-stricken area can provide unique and critical information for
emergency responders and other diverse stakeholders. Kogan et al. (2015) and Starbird and Palen (2010) foundthat Twitter users, in general, were more likely to re-tweet the accounts of people local to emergency events. Bica
et al. (2017) investigated an imaginary representation of a disaster via images shared on Twitter. They found that
locals focus more on the business details of the response and the damage in their cities while a non-local population
focus more on the images of people suffering (Bica et al., 2017). In addition, there have been various efforts tomake it easier to find communication posted by people local to disaster-stricken areas. For example, Grace et al.
(2017) investigate how to effectively discover local populations by using social network ties. Other typical ways
to identify local communications are using disaster-related keywords and location-based tweet collections(Olteanu et al., 2014). This study also applies the keyword-based tweet collection and divides local and non-local
communication according to whether communication contains disaster-impacted locations’ names (details aredescribed in the Data section).
RESEARCH QUESTIONS

Based on the previous studies cited above, this study investigates the following three research questions. First,
although only limited research has been done on whether or not we can improve situational awareness during
recovery phases and how we can do so, using social media data as a sensor of recovery activities has the potential
to improve recovery efforts because social-media generated information may be able to complement existing
recovery indicators by its timeliness. Thus, for the first step to explore the possibility of using social media data
to detect recovery activities, we analyze whether there were correlations between people’s communication in thevirtual world and people’s activities in the real world.


RQ1: Did social media communication topics have relationships with the excess demand for used carsand housing in the disaster-stricken area?

Secondly, although both used cars and housing are likely to be in higher demand after a large-scale water-related
disaster, the two goods have different characteristics and thus, when and which people in the disaster-stricken area
needed to buy these two goods may be different. In this study, our hypothesis is that the two types of goods had
different types of correlations with social media communication.


RQ2: Did the excess demand for used cars and the excess demand for housing correlate differently withsocial media communication?

Lastly, related works investigating relatively short-term restoration and response found the different types ofcommunication patterns between people local to a disaster-stricken area and people non-local to the disaster. Our
hypothesis in this study is that even during recovery phases, the local and non-local population communicate
differently, and thus we consider the following RQ3.


RQ3: Did social media communication posted by people local and non-local to the disaster-stricken area
have different types of correlations with the excess demand for used cars and housing?
CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
Zeno Franco, José J. González and José H. Canós, eds.

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Detecting Disaster Recovery Activities via Social Media
Communication Topics

DATA
Socio-economic Recovery Activity Data
Used-car market data

As one of the proxies of socio-economic recovery activities, we use the Japanese used-car market data sourced
from advertisements posted on one of the most dominant used-car magazines in Japan, “Goo4,” which is publishedevery two weeks. We used the data posted by dealers in the tsunami-damaged areas for six months after thedisaster. For the analysis, we selected a body type named Light Motor Vehicle Cab Van because both quantitative
research (Shibuya and Tanaka, 2019) and qualitative reports, such as interviews in the newspaper and ingovernment reports showed that there was an excess demand for the Light Motor Vehicle Cab Van. The data
include used-car characteristics such as age, price, and transmission. The real prices of used cars were calculated
based on the fiscal 2015 Consumer Price Index5.
Housing market data

This study uses the leased housing market data from six months after the disaster provided by At Home Co., Ltd6.
The data include housing characteristics such as the lease price, the size of the building area, the age of a property,
and the address. The lease prices were adjusted as real prices with the residential property price index7. To exclude
outliers, the real sales price per square meters of each property was calculated. Properties whose price per square
meter was more than or less than the mean real sales price ± the standard deviation × 4 were excluded. Each
property was checked to see if it was in the building damaged zones, and if not, the distance to the building damage
zones was calculated based on the survey results of building damages conducted by the Ministry of Land,
Infrastructure, Transport, and Tourism8. The distance from each property to Sendai station as a proxy of the city
center was calculated. The minutes from the nearest station of each property to Sendai station by a train was also
added as a feature. The current study only uses the leased houses located in plains and within 3km to the building
damage zones because the quantitative research (Shibuya and Tanaka, 2018c) reveals that there was an excess
demand for leased properties, particularly those located in plains and within 3km to the building damage zones.
Table 1 describes the numbers of used cars and housing for the analysis every two weeks.
Table 1. Observation numbers of used cars and housing every two weeks
Mar-2 Apr-1 Apr-2 May-1 May-2 Jun-1 Jun-2 Jly-1 Jly-2 Aug-1 Aug-2
Used Cars 14
Housing 656

118
287

132
436

124
343

129
468

114
389

114
344

104
363

104
349

134
309

106
348

Sep-1
121
266

Note 1: Mar-1 represents the first half of March; Mar-2 represents the second half of March. The same applies hereafter.
Note 2: In this study, we set a one-month time lag for used-car data because our used-car target data are sourced from a paper-based magazine.
It took about one month for the magazine to be organized and to publish the data after collecting data from town shops. For example, in thisstudy, used-car data from the second half of April (Apr-2) is treated as the second half of March (Mar-2). On the other hand, we did not set
any time lags for housing market data because they are sourced from an online platform, and they were published in real-time way.

Social Media Communication Data: Twitter Data

Twitter is a platform that allows users to post messages (tweets) to their followers and the public (if a user permits),
and to receive posted messages from other users. The target data in this study were collected through an Internet
service called “Kuchikomi@kakaricho” (http://kakaricho.jp)9. We selected Japanese tweets posted for six months
after the disaster (March 11th to September 11th of 2011) that contained at least one of the 16 Japanese keywords
deemed to be relevant to our study purposes:10 “earthquake,” “disaster,” “affected,” “evacuation,” “thank you,”“temporal,” “restart,” “recovery,” “information,” “appreciation,” “support,” “tsunami,” “damaged,” “car,” “light,”
4 The used-car data were provided by “Proto corporation” which publish “Goo.”
5 We used “automobile” deflator sourced from http://www.e-stat.go.jp/SG1/estat/List.do?bid=000001074278&cycode=0 (accessed October

17, 2018).
6 The study is supported by Joint Research Program No.823 at CSIC, The University of Tokyo (“Real Estate Database 1999-2016” by At

Home Co., Ltd.)

7 Average of 2010 = 100, http://www.mlit.go.jp/totikensangyo/totikensangyo_tk5_000085.html, accessed September 21st, 2018
8 http://www.mlit.go.jp/toshi/toshi-hukkou-arkaibu.html
9 Although Twitter API allows collecting public tweets, we cannot collect tweets seven years ago (when the disaster happened). Therefore,

we use an internet service Kuchikomi@kakaricho which provides historical tweets.
10 We applied keywords-based tweets search approach based on related works (e.g., Wendland et al, 2017; Musaey et al., 2017; Alam et al.,2017). Before selecting the 16 keywords, the authors manually checked various types of tweets relevant to the disaster. After discussing theappropriateness of the keywords among authors, we carefully chose 16 Japanese keywords.

CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
Zeno Franco, José J. González and José H. Canós, eds.

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Detecting Disaster Recovery Activities via Social Media
Communication Topics“evacuation” (all keywords are translated by the authors). In total, we collected 11,676,929 tweets.
Selection of disaster-related communication based on machine-learning

To select social media communication that related to the disaster, we classify the originally collected data basedon machine learning techniques. First, after removing duplicated and re-tweeted tweets from the initially collected
data, we randomly sampled a subset of about 2,000 tweets. After manually annotating the subset of 2,000 tweets
based on whether the subset was disaster-related or not, we developed several classifiers including Naive Bayes,
Support Vector Machine (SVM), and Logistic Regression along with a 10-fold cross-validation technique tocompare the accuracy of these classification models. We found that the Naive Bayes classifier is the best predictor(Accuracy: 0.932, Precision: 0.945, Recall: 0.939)11. We then predicted and labeled the rest of the Twitter datawith the Naive Bayes classifier. As a result, this study labeled 817,084 tweets as disaster-related. For further
research, we only use these disaster-related labeled tweets.
Table 2. The Number of Local and Non-Local Tweets Datasets
Number of Tweets
Local
Non-Local

Mar-2 Apr-1 Apr-2 May-1 May-2 Jun-1 Jun-2 July-1 July-2 Aug-1 Aug-2
Sep-1
728
549
393
529
401
357
415
385
322
326
307
463
209,346 94,748 60,211 48,653 47,174 49,436 43,040 50,053 42,699 41,301 45,270 79,978

Selection of tweets posted from the local and non-local population

As a means to examine whether a communication posted by the local and non-local population had different typesof relationships with the excess demand for used cars and houses (RQ3), we further categorized the data based on
tweets with geo-information. We made two datasets; a Local Tweets dataset and a Non-Local Tweets dataset. The
Local Tweets dataset consists of tweets either whose tweeters’ profile shows that they were in tsunami-stricken
cities12, their profile description contains any of the tsunami-stricken cities’ names, or tweets with geo-tagged
information that declares that they were in the tsunami-stricken area. The Non-Local Tweets dataset are theremainder of the data. Table 2 shows the number of each dataset every two weeks.

Figure 1. Topic Frequency ratio (Ttn) of Tweets Posted by Local and Non-Local Populations

11 SVM: Accuracy: 0.592, Precision: 0.592, Recall: 0.992. Logistic Regression: Accuracy: 0.928, Precision: 0.933, Recall: 0.9458.
12 This study uses tsunami-stricken cities’ names listed on the “Area of inundated area” by the Geospatial Information Authority of Japan(http://www.gsi.go.jp/kikaku/kikaku60004.html). We only used tsunami-stricken cities’ names in Miyagi prefecture and Iwate prefecture.
Fukushima prefecture was also one of the devastated areas of the disaster, but we did not include cities in Fukushima prefecture because
Fukushima prefecture suffered more from the nuclear incident and thus, it should be treated differently.

CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
Zeno Franco, José J. González and José H. Canós, eds.

710

Shibuya et al

Detecting Disaster Recovery Activities via Social Media
Communication Topics

Table 3. LDA topics
Top 10 most probable words Tweet examples
0 Electricity/nuclear restart, power saving, summer, “Now is not the time to criticize the government, but
rice, electricity, non-nuclear
power plant
the time to look at the reality and figure out what we
power, shortage, Kesennuma(city name), Hokkaido (region have to do. Radioactive substances are detected but weneed electricity, and we cannot store it up.”name), machine
think, oneself, thing, myself,“I think it is unfair that you need a local government’s
1 One’s opinion
say, appreciation, watch, what, permission to send relief supplies. I cannot believe that.
now, direction
We have watched affected towns suffering, and I thinkthey have no time to give permission to individuals. ”“The Councilors’ budget committee will restart the day
2 Political recovery free, support, person, service,
site, governor, consultation,
supportafter tomorrow. I heard that members are preparing to
minster, freely, photoconsult about the disaster-stricken area’s situations andhow to support them.”
recovery, nuclear power, Japan, “I met an elderly person who evacuated from the
3 Nuclear-related
evacuation, country,
activities
nuclear-affected area. The lifeline utilities are in need
Fukushima, issue, say,of repair, and we still have family there.”government, China
evacuation, children, child,“Several people asked us to evacuate to another4 Evacuation
shelter, kid, target, need,country.”, “Evacuation places are in need of diapers,
parent, work, school
water, and rice.”
affected-area,
affected,“To support recovery efforts, the company bought a
5 Recovery
recovery, land, use, message,
boatload of fish.”think, smile, Japan, support
use, car, vehicle, tax increase, “We need to send gasoline for cars to the disaster6 Means ofrestoration, accident,
transportation
stricken area. “Timely recognition of priority issues istechnology, automobile,needed to send relief supplies to the disaster-stricken
ization/ize, needarea. “The train line in the disaster-stricken area ismoving towards recovery.”car, go, think, come, inside,“I went back to the disaster-affected area and saw many
7 Going to thedisaster-stricken time, house, evacuation, go out, volunteers who came and helped with the cleaning.”
myselfarea“Does anyone know the website informing people how
to go or send supplies to the disaster-stricken area?”
support, affected area, the Great “If anybody is looking for a place to sleep, please come
8 Support
East Japan Earthquake andto Nihonmatsu town. There is a shelter.” “We need to
Tsunami, recovery, direction,
support one another.” “Photographers are holding anactivity, volunteer,
participation, recovery support exhibition to support the disaster-affected area.”“The height of the first tsunami was over 10 meters.
9 Disaster damages tsunami, earthquake damage,evacuation, information,
The second one was over five meters.” “Teachers andoccurrence, news, the Greatchildren of the school are still left behind on the third
East Japan Earthquake andTsunami, Mainichi newspaper, floor of the building in Miyagi prefecture.”
Miyagi prefecture# Proposed label

Note: All original tweets are in Japanese. All tweets in the table are trasnlated by the authors.
Topic Analysis: Latent Dirichlet Allocation (LDA)

Before conducting the primary analysis, we conducted topic analysis to explore what kinds of topics were on
Twitter after the disaster. In doing so, we applied the Latent Dirichlet Allocation (LDA). LDA is a probabilistic
topic modeling widely used in natural language processing to summarize and extract topics from documents. The
intuition behind LDA is that documents exhibit multiple topics and a topic should be a distribution over a fixedvocabulary (Blei, 2012). When we manually looked into various tweets from the target dataset, we found each
tweet could be categorized into several topics (for example, a tweet contains two topics; the disaster’s damageand emotional encouragement). Thus, LDA, which assumes that one document contains multiple topics, waschosen for this analysis. By treating one tweet as one document, we conducted LDA with ten topics for the

CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
Zeno Franco, José J. González and José H. Canós, eds.

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Detecting Disaster Recovery Activities via Social Media
Communication Topics

predetermined topic number13. The ten topics found by LDA and the most probable terms and proposed labels areshown in Table 3. Next, every tweet in our dataset was automatically labeled with the ten topics based on the
results of LDA; If a tweet had more than 10% probability of being categorized as topic n, the tweet was labeled
as the topic of n related. Next, the topic frequency ratio (Ttn) for every two weeks was calculated as follows: !!" =#$%&' )*+,),- #.,,!!"/)) #.,,!!

where #$$ !&''(! denotes the number of all tweets in t period and !)*+, $-.'$'/ !&''(!"

denotes the number of tweets labeled with the topic of n in t period. Figure 1 describes the chronological changesof the topic frequency ratio (Ttn) of tweets posted by local and non-local populations, respectively.
Table 4. The statistical summary of equation (1) for each body type (six months pooled data)
Used Cars (n=1,314)

Housing (n = 4,558)

mean

std

min

max

mean

lnP
X1
X2

13.07
0.29
0.00

0.53
0.45
0.06

11.00
0.00
0.00

14.20
1.00
1.00

std

min

max

lnP
X1
X2

10.63
0.68
0.01

0.31
0.47
0.00

8.82
0.00
0.00

11.88
1.00
0.03

X3
X4

7.37
0.31

4.25
0.46

0.00
0.00

21.00
1.00

X3
X4

0.02
0.02

0.04
0.01

0.00
0.00

0.17
0.12

L0
L1
L2

0.14
0.27
0.24

0.02
0.02
0.03

0.12
0.22
0.2

0.18
0.31
0.29

X5
X6
X7

0.02
0.03
0.00

0.01
0.01
0.00

0.00
0.01
0.00

0.07
0.17
0.02

L3
L4

0.62
0.13

0.04
0.03

0.54
0.08

0.68
0.18

L0
L1

0.15
0.27

0.02
0.02

0.12
0.24

0.17
0.31

L5
L6
L7

0.4
0.18
0.56

0.04
0.02
0.04

0.34
0.14
0.49

0.48
0.23
0.61

L2
L3
L4

0.24
0.64
0.13

0.03
0.03
0.03

0.2
0.59
0.08

0.29
0.68
0.18

L8
L9

0.15
0.52

0.02
0.02

0.12
0.48

0.19
0.56

L5
L6

0.4
0.18

0.04
0.02

0.34
0.16

0.48
0.22

NL0
NL1

0.25
0.32

0.02
0.02

0.21
0.25

0.31
0.35

L7
L8

0.54
0.14

0.02
0.02

0.49
0.12

0.56
0.17

NL2
NL3
NL4

0.25
0.7
0.1

0.02
0.02
0.01

0.23
0.67
0.09

0.28
0.74
0.13

L9
NL0
NL1

0.52
0.26
0.31

0.03
0.03
0.02

0.48
0.22
0.29

0.56
0.31
0.33

NL5
NL6

0.3
0.25

0.01
0.02

0.27
0.2

0.34
0.3

NL2
NL3

0.24
0.71

0.01
0.02

0.23
0.67

0.26
0.74

NL7
NL8
NL9

0.39
0.13
0.48

0.04
0.01
0.02

0.33
0.12
0.45

0.46
0.14
0.51

NL4
NL5
NL6

0.1
0.3
0.26

0.02
0.02
0.03

0.09
0.27
0.23

0.13
0.32
0.3

NL7
NL8

0.38
0.13

0.04
0.01

0.33
0.12

0.43
0.14

NL9
0.48
0.02
0.45
0.52
Note 1: Lk denotes the topic frequency ratio of topic k within the Local Tweets dataset. NLk in the table denotes the topic frequency ratioof the topic k within the Non-Local Tweets dataset.

MODEL

To address how the topic frequency ratio on Twitter correlated with used-car prices, we developed a model below(equation 1) based on the Hedonic model as described in the section on Related Literature. By controlling usedcar/housing characteristics, such as age and how many kilometers were driven, the Hedonic model allows us toassess the correlation between the used-car/housing prices, which reflect the demand for used-car/housing, andthe topic frequency ratios on Twitter.
ln 2& = 30 + 31 51& + 32 !2& + 6&

(1)

where lnPi is the natural logarithm of the real price of the i th product, Xji is a vector of j th observablecharacteristics of the used cars or the houses. Tki is the k th topic frequency ratio (L0 to T9 and NL0 to NL9 in

13 To decide a suitable number of topics, the author applied LDA with several topic numbers, including 10, 20, 30, 50 and 100. As a result,when the topic number is 10, the results are most convincing and understandable to people although the perplexity is not the best.

CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
Zeno Franco, José J. González and José H. Canós, eds.

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Table 4). 6& is the error term. We apply equation 1 to each topic frequency ratio14 and used-car and housing market
data respectively. For the used-car market data, the following control variables Xi are used:
Transmission:
X1i = Transmission Dummy (Automatic=1, others=0)
Fuel:
X2i = Special types of fuels Dummy (LPG, CNG or FC=1, others = 0)Age:
X3i = Age (in years)
Kilometers driven:
X4i = 100,000km Dummy (over 100k km driven=1, others=0)
For the housing market data, the following control variables Xi are used:
X1i = Apartment dummy (apartment = 1, others = 0)
X2i = Distance to Sendai station (1/1000 km)
X3i = Time from the nearest station to Sendai station by a train/subway (1/1000 minutes)
X4i = Age (1/1000 year)
X5i = Accessibility to the nearest station by walking (1/1000 min)
X6i = Size of building area (1/1000 m2)
X7i = Height of a property (the sum of property’s levels × 1/1000)
The statistical summary of our model is shown in Table 4.
RESULT

Table 5 shows a summary of the estimated coefficients of the topic frequency ratios (Tki) of equation 115. In thetable, if an estimated coefficient of a topic frequency ratio has a significant correlation with the excess demand of
used cars or housing at a 5% significance level, the cell is shaded. Other estimated coefficients of our model andadjusted R2 are presented in Tables 6 and 7.
Regarding the excess demand for used cars, there were significant positive correlations between the used-car
demand and the topic frequency ratio of Topic 3 (Nuclear-related activities), Topic 5 (Recovery), and Topic 9(Disaster damages) within the Local Tweets dataset. On the other hand, there were significant negative
correlations between the used-car demand and the topic frequency ratio of Topic 4 (Evacuation) within the Local
Tweets dataset. In contrast, there were significant positive correlations of the topic frequency ratio of Topic 7(Going to the disaster-stricken area) and Topic 8 (Support) with the prices of used cars within the Non-local
Tweets dataset.
Regarding excess demand for housing, there were statistically significant positive correlations between the excess
demand for housing and the topic frequency ratio of Topic 1 (Electricity/nuclear power plant) and Topic 6 (Meansof transportation/ logistics) within the Local Tweets dataset. On the other hand, we did not find any significant
correlations between the demand for housing and the topic frequency ratios within the Non-Local Tweets dataset.
Table 5. Estimated Coefficients of Topic Frequency Ratio
Topic0Local
NonLocal
Local
Housing NonLocal
Used
cars

Topic1

Topic2

Topic3Topic4

Topic5Topic6

Topic7

Topic8Topic9Going tothe
NuclearMeans ofrelated Evacuation Recovery transportat disasterstricken
activities
ion
area

SupportDisasterdamages

Electricity/
nuclear
power
plant

One’s
opinionPolitical
recovery
support

0.19

-0.49

0.06

0.56**-1.08***

0.64**

-0.62

-0.41

-0.48

0.96**

-0.37

-0.06

0.41

-1.05

0.42

0.74

-0.51

0.94***

3.22**

0.48

-0.14

0.25***-0.11-0.16**

0.07

-0.07

0.24**

0.13

-0.12

-0.05

-0.08

0.08

0.23

-0.2

0.1

-0.07

-0.06

-0.04

-0.01

0.01

Note1: **: p < 0.05, ***: p < 0.01. The same applies hereafter.
14 We apply equation (1) to each topic frequency ratio respectively because the correlations between each topic are from 0.08 to 0.8 andseveral topic frequency ratios would cause multicollinearity.
15 We note that this study uses relatively large numbers of observations, and statistical significances (p-values) do not directly estimate thestrength of related variables. Among the statistically significant topic frequency ratios (Tk), the largest effect size (Cohen’s f2) is 0.01. Futurestudies need to analyze how to improve accuracy of estimating disaster recovery activities with social media related variables by, forexample, applying other social media related variables and by applying different models.

CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
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Note2: We found that both heteroskedasticity and serial correlation exist (Breusch-Pagan statistics’ p-value are all less than 0.01. DurbinWatson statistics’ p-values are less than 0.01). Therefore, the standard errors are corrected for serial correlation and heteroscedasticity byusing the Newey-West procedure.
Note3: The Variance Inflation Factor (VIF) of each regression for the housing datasets is from 1.00 to 2.08. The VIF of each regression forused cars is from 1.00 to 1.18. These VIF scores suggest there are no multicollinearity problems.

Table 6. The results of equation (1) within the Local Tweets dataset

Used cars

Adj.R20.70
Adj.R20.70
Adj.R20.70
Adj.R20.70

X1
0.04**
0.02
X1

X2
0.05
0.19
X2

X3
-0.08***
0.00
X3

X4 Topic3-0.35***
0.56**
0.02
0.26
X4 Topic4

Estimate
Std. Error

13.91***
0.05
Intercept

0.04*
0.02
X1

0.07
0.17
X2

-0.08***
0.00
X3-0.36*** -1.08***
0.02
0.40
X4 Topic5

Estimate
Std. Error

13.52***
0.11
Intercept
13.28***
0.19
Intercept
10.43***
0.03
Intercept
10.60***
0.04
Intercept
10.46***
0.03

0.04**
0.02
X1
0.04**
0.02
X1-0.07***
0.01
X1-0.07***
0.01
X1-0.07***
0.01

0.08
0.18
X2
0.06
0.18
X2
-4.49***
0.72
X2-4.47***
0.72
X2-4.44***
0.72-0.08*** -0.35***
0.64**
0.00
0.02
0.27
X3
X4 Topic9-0.08*** -0.35***
0.96**
0.00
0.02
0.38
X3
X4
X5
X6
X7 Topic1-0.33*** -14.45*** -2.07*** 14.89*** 10.72*** 0.25***
0.09
0.61
0.23
0.45
1.99
0.09
X3
X4
X5
X6
X7 Topic3-0.34*** -14.47*** -2.07*** 14.87*** 10.83*** -0.16**
0.09
0.61
0.23
0.45
1.99
0.06
X3
X4
X5
X6
X7 Topic6-0.33*** -14.45*** -2.05*** 14.86*** 10.89*** 0.24**
0.09
0.61
0.23
0.45
1.98
0.11

Estimate
Std. Error

Adj.R2
0.71
Housing

Intercept
13.42***
0.17
Intercept

Estimate
Std. Error

Estimate
Std. Error

Adj.R20.71

Estimate
Std. Error

Adj.R20.71

Estimate
Std. Error

Table 7. The results of equation (1) within the Non-Local Tweets dataset
Used cars

Adj.R2Estimate
0.70
Std. Error
Adj.R2Estimate
0.70
Std. Error

Intercept
13.41***
0.14
Intercept
13.35***
0.20

X1
0.03*
0.02
X1
0.04*
0.02

X2
0.07
0.19
X2
0.05
0.18

X3
-0.08***
0.00
X3-0.08***
0.00

X4-0.35***
0.02
X4-0.35***
0.02

Topic7
0.94***
0.34
Topic8
3.22**
1.48

DISCUSSION

By applying the Hedonic model, this study observed whether there were correlations between social media
communication topics and the excess demand for used cars and housing as proxies of socio-economic recovery
activities in the disaster-stricken area (RQ1). Also, we analyzed whether the excess demand for used cars andhousing correlated differently with communication topics (RQ2) and whether there were any differences in social
media communication posted by people local and non-local to the disaster-stricken area (RQ3). We present our
discussion according to these research questions.
Regarding RQ1, by applying equation 1, we found that several topic frequency ratios had statistically significant
correlations with the excess demand for used cars and housing. These results imply that the topic appearance ratios
on Twitter might be useful in detecting people’s socio-economic recovery activities in the real world.
Regarding RQ2, the results suggest that the different types of the two goods correlated to different topic frequency
ratios. To be specific, regarding used cars, we found that the excess demand for used cars correlated to thefrequency ratios of topics related to nuclear-related activities (Topic 3), evacuation (Topic 4), recovery (Topics 5)and damage (Topic 9) within the Local Tweets dataset. Also, the frequency ratios of topics related to going to(Topic 7) and supporting (Topic 8) the disaster-stricken area within the Non-Local Tweets dataset correlated withthe excess demand for used cars. On the other hand, regarding housing, we found that the excess demand for
housing correlated to frequency ratios of topics related to people’s opinion (Topic1), nuclear activities (Topic3)
and means of transportation/logistics (Topic 6) within the Local Tweets dataset. It is interesting to focus on topics
related to nuclear activities (Topic 3) because the excess demand for both goods, used cars and housing, correlatedwith the topics (Topic3), but they correlated in different ways: The excess demand for used cars had positive
correlations with nuclear activities while the excess demand for housing had negative correlations with the topic.
This implies that because nuclear-related issues caused anxiety among people in the disaster-stricken area, they
started to rent new places to resettle when uncertainty about nuclear-related issues gradually diminished. On theother hand, locals might have started to purchase used cars even while there were concerns related to nuclearCoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
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Communication Topics

related issues.
Regarding RQ3, the analysis implies that communication posted by locals and non-locals correlated differently
with the excess demand for used cars. This study found that there were negative correlations between the usedcars and the frequency ratios of topics related to evacuation (Topic 4). This implies that when people in thedisaster-stricken area were still in evacuation shelters, there was less need to obtain or replace vehicles. Moreover,
we found positive correlations between the excess demand for used cars and the frequency ratios of topics related
to nuclear-related activities (Topics 3), recovery (Table 5) and damage (Topic 9) within the Local Tweets dataset,and topics related to going to and supporting the disaster-stricken area (Topics 7&8) within the Non-Local Tweets
dataset. This implies that the local population might have communicated more about nuclear-related activities(Topic 3), recovery (Topics 5) and damage (Topic 9) while people became active in recovery efforts and restarted
their daily lives as observed in the excess demand for used cars. Meanwhile, people outside of the disaster-strickenarea tended to communicate more about going to and supporting the affected area (Topics 7&8). These results are
similar to the findings of the previous study that found local people tended to share images of response, recovery
and damages while the non-local population tended to share images of people suffering (Bica et al., 2017). Thisis because local residents needed to face realities and be strong in order to return back to normal routines whilepeople from the outside may have tried to support the disaster-stricken area because they knew of local people’s
continuous struggles to recover. Our results imply that, for recovery phases, it is also important to distinguishcommunications posted by locals and non-locals.
In addition, regarding housing demand, we found positive correlations between the excess demand for housing
and the frequency ratios of topics related to people’s opinions (Topic1) and the means of transportation/logistics(Topic 6) within the Local Tweets dataset while there were no correlations between topic frequency ratios within
the Non-Local Tweets dataset and the excess demand for housing. These results imply that people in the disasterstricken area started to express their opinions about the situations surrounding them, such as recovery progress
while people in the disaster-stricken area started to resettle as observed in the excess demand for housing.
Although it is difficult to interpret the correlations between the excess demand for housing and the topic frequency
ratios of topics related to transportation means (Topic6), this result might be reflected in the contents of tweets
labeled as Topic 6. The tweets categorized as Topic 6 include less communication about car scarcity and the needto buy cars while Topic 6 tweets include more communication about a lack of gas, which might have tended tooccur after people purchased used cars, and more communication about public transportation system recovery
updates (e.g., tweets sharing the information about train service status).
CONCLUSION

The object of this study was to address how social media data could be used for detecting recovery in disasterstricken communities. We empirically analyzed the correlations between topic frequency variations in disasterrelated tweets after the Great East Japan Earthquake and Tsunami of 2011 and two types of proxies of socioeconomic recovery activities, namely the excess demand for used cars and the excess demand for housing. The
results showed several statistically significant correlations between social media communication topic frequency
ratios and the excess demand for both used cars and housing. These results provide evidence to support thepossibility of using social media communication for detecting socio-economic recovery activities in a disasterstricken area. Furthermore, the excess demands for two different types of goods (used cars and housing) had
different types of correlations with socio-economic recovery activities. More specifically, our study implies thatpeople in the disaster-stricken area started to be active in recovering and restarting their daily-lives as observed in
the excess demand for used cars when there were more communications related to recovery and disaster damage
among them. Also, during those periods, there tended to be more communication related to going to andsupporting the affected area among the non-local population. In contrast, when the local population started toresettle, they might have communicated more opinions about their situations. Furthermore, our results imply that
tweets posted by people local and non-local to the disaster-stricken area might have had different types ofcorrelations with the socio-economic recovery activities.
The academic contribution of this study is that we were able to show the potential usefulness of social media for
detecting socio-economic recovery activities, which has not been adequately studied among previous related
studies. Furthermore, we were able to demonstrate that communication posted by local and non-local populations
had different types of correlations with people’s activities in the real world. This indicates that when we studysocial media communication for recovery, different communication patterns among local and non-local
populations should be considered.
However, the authors acknowledge that this work is the first of its kind to examine the possibility of using social
media data for detecting recovery activities. There are several limitations. This study collected tweets that
contained specific words related to the disaster and disaster recovery activities as explained in the Data section
and thus, the keywords might have affected the overall findings. In addition, we note that there is a likelihood of
there being other intervening variables for topic frequency ratios. Further research is needed to analyze causality
CoRe Paper – T8 – Social Media in Crises and Conflicts
Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
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and direct relationships between topic frequency ratios and recovery activities. There is a need to expand thecurrent study by, for example, analyzing the relationships between social media data and other types of recovery
activities in the real world, as well as investigating recent large-scale disasters to understand general baselines ofdisaster recovery activities. Extracting variable communication topics which are beneficial for disaster responders
and managers is another important study topic to demonstrate how social media could be used in all the stages ofdisaster management. Future studies also need to find effective way to distinguish between posts by locals fromthose by non-locals.
ACKNOWLEDGEMENTS

This study was partially supported by a research grant, “Proto Award.”. The author would like to show gratitude
to the Proto Corporation for providing various used-car market data and financial support. This study is also
partially supported by the Graduate Program for Social ICT Global Creative Leaders (GCL). In addition, the study
is supported by Joint Research Program No.823 at CSIC, The University of Tokyo (“Real Estate Database 19992016” by At Home Co., Ltd.).
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Proceedings of the 16th ISCRAM Conference – València, Spain, May 2019
Zeno Franco, José J. González and José H. Canós, eds.

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