International Journal of Informatics Society, VOL.5, NO.3 (2013) 137-145

137

Toward the operation guidelines for municipal social media account: From theanalysis of user’s behavior on Twitter in the Great East Japan Earthquake
Tomoo Inoue††

Faculty of Library, Information and Media Science, University of Tsukuba, Japaninoue@slis.tsukuba.ac.jp

Abstract – More and more municipal organizations havebeen using Twitter for information distribution or interactive
information exchange. Such official information sources aresupposed to be relied upon by citizens when a disasterbreaks out, but how such information on social media shouldbe operated is not known.
This research investigated the user behavior around theTwitter accounts of municipal organizations in the Great
East Japan Earthquake. Through the close investigation, theoperation guidelines for a municipal Twitter account wereobtained.
Keywords: Social media, Twitter, Information behavior, theGreat East Japan Earthquake, Guideline

1

INTRODUCTION

Together with the rising popularity of social networking
services, a micro blogging service Twitter had been used
extensively when the Great East Japan Earthquake happened
on March 11, 2011.
There were a few types of Twitter use; information
broadcasting to public, information receiving from public,
and information exchange among closed members. They
were useful when other media disclosed their own
weaknesses. The mass media worked well for nation-wide
broadcasting but not so much for regional broadcasting. The
phone line was overloaded to connect [1][2]. People were
anxious about what was happening, what would be going on,
and wanted the information. Twitter matched to fulfill theseneeds.
As for the Twitter use by municipal organizations, it was
still in the beginning stage of big growth at that time. Someaccounts were relatively new while some other already had anumber of followers. Soon after the disaster, we gained
increasing momentum to use such social media. Morenumbers of municipal organizations officially started to use
social media, especially Twitter. Recently the government
officially encouraged municipal organizations to use social
media for their information broadcasting [3].
However, we do not know exactly how municipal Twitter
account worked, and thus we do not know how municipal
social media account could be operated effectively so thatthe account is useful to the public, especially in emergency.
To answer this question, we investigate the user’s behavior
on Twitter in the Great East Japan Earthquake, and draw theoperation guidelines for municipal social media account.

2

RELATED WORKS

2.1

Twitter

Twitter is an online microblogging service. It provides
almost real-time online short text communication. It can beused from various internet devices including a personal
computer, a smart phone, and other mobile phones. A shorttext message called a “tweet” can be viewed from the world
if it is not sent from the specially closed account. This
normal tweet can be searched by keywords, tags, andaccounts. Subscribing an account called “following” is afeature of Twitter. By this, a subscriber called a “follower”
can view the tweets of the “following” account without any
operation. Thus an account that sends useful information
regularly to the receiver, or that “tweets” useful information
regularly, is likely to be followed. When a receiver of atweet thinks to distribute the tweet, he/she can tweet that
tweet. This action is called “retweet.”

2.2 Twitter Use in Disaster
Mendoza et al. studied the behavior of Twitter users in the2010 Chilean earthquake in the hours and days following
this disaster [4]. They performed a preliminary study of thedissemination of false rumors and confirmed news. They
analyzed how this information propagated through theTwitter network to assess its reliability as an information
source under extreme circumstances, and showed that the
propagation of rumors differed from that of news because
rumors tended to be questioned more than news by theTwitter community.
Longueville et al. studied how Twitter could be used as areliable source of spatio-temporal information by focusing
on the 2009 French forest fire, aiming to demonstrate its
possible role to support emergency planning, risk assessment
and damage assessment activities [5]. They studied thetemporal dynamics of the tweets, how location names werecited, who published the tweets, what type of domains werecited as URLs.
Vieweg et al. analyzed the tweets generated during two
concurrent emergency events in North America 2009, theOklahoma Grassfires of April and the Red River Floods in
March and April [6]. They focused on communications
broadcast by people on the ground, investigated the tweets interms of location information and update with theirdissemination. They then identified information that might
contribute to enhancing situational awareness during
emergencies.

ISSN1883-4566 © 2013 - Informatics Society and the authors. All rights reserved.

T. Inoue / Toward the operation guidelines for municipal social media account:
From the analysis of user’s behavior on Twitter in the Great East Japan Earthquake

138

Qu et al. studied the messages in a microblogging site
Sina-Weibo that is very similar with Twitter and is popular
in China in the 2010 Yushu Earthquake [7]. They
investigated the content of the messages, the trend ofdifferent topics, and the information spreading process.
Miyabe et al. studied the tweets in the Great East Japan
Earthquake in terms of their locations [8]. They concluded
how people used Twitter was different from the locations
depending on the damages.
Umejima et al. studied the false rumors and theircorrections in a disaster, and indicated that a false rumor iseasier to disseminate [9].
These studies analyzed the Twitter uses by people at large
in emergency cases, where this study focuses on the use ofmunicipal accounts in emergency cases.

2.3 Twitter Use by Public Institutions
Alam et al. studied six governmental Twitter accounts inAustralia [10]. They analyzed the content of the tweets aswell as the responses from the citizens.
Kavanaugh et al. studied the local governmental use ofsocial media including Twitter [11]. They conducted asurvey to local governmental institutions, the analysis of thefollowers of governmental Twitter accounts, and the analysisof the messages in Facebook accounts.
These studies analyzed the use of Twitter by public
institutions in normal circumstances, while our study focuseson the Twitter use in emergency.

3
3.1

DATA AND ANALYSIS
Data

The analyzed data were from the Twitter accounts thatwere registered in “Govtter,” the link site of official
municipal Twitter accounts [12]. Govtter only lists theTwitter accounts that were confirmed as those operated bygovernmental institutions. A total of 363 accounts werelisted as of January 2012 including the municipal accounts,
the governmental accounts, and other public institutions.
Among those accounts, 149 accounts were older than March
11th of 2011, before the earthquake.
In Twitter, a tweet is stored with the links to the date andtime of the tweet, the identifier number called Status ID, andthe account information. These tweets could be acquired
from the latest tweet to as far back as 3200 tweets by Twitter
API in the official site (in 2011 when the data was collected)[13]. However the number of follows and the number offollowers in the account information were those at the timeof getting the tweet data, which meant chronological changeof those values could not be obtained by this method.
Thus we used Twilog service to obtain the change of thenumber of followers [14]. In Twilog, the number of follows
and the number of followers are recorded along with thetweet itself for the registered Twitter accounts. We used all
the data of the municipal accounts that were listed in Govtter
and registered to Twilog before March 11th of 2011, whichcounted to 34 accounts.

The data were obtained from February 1 to April 30 in
2011. The total number of the tweets was 42825.

3.2

Analysis

The data was analyzed in the following way.
First the outline of the data was obtained. It was about thetweet and follower in this study. The basic structure of
information behavior is sending information and receiving
information. Information is sent by a tweet. Thus the numberof tweet was used for analysis. If a person wants to receive
the information from an account, the person follows theaccount. This action is reflected as the number of followers
across time, the increasing rate of followers in other words.
Thus the increasing rate of followers was used for analysis.
After getting the outline of the number of tweets and thenumber of followers for all the accounts, the accounts werecategorized into four groups in quantitative viewpointaccording to the number of the tweets and the number offollowers across time. The average values of these metrics
were used as the borderlines of the groups, which iscommon in explorative data analysis. Each group wasinvestigated accordingly.
Furthermore, the content of the tweets were investigated.
They were categorized in five in qualitative viewpoint. The
relation between the groups and the content categories werealso discussed.

4

RESULT

4.1 Change of the Number of Tweets andFollowers
The change of the number of tweets for all the 34 accounts
is shown in Fig. 1. The account names in the legend aresimply in alphabetical order. Because a tweet bot “Obsekuri”
which was an extremely frequent tweeter than otheraccounts is included in Fig. 1, it is difficult to see the data ofother accounts. To get the general outline of the change ofthe number of tweets, the account was removed from thegraph and Fig. 2 is shown.
The Change of the number of followers for all the accounts
is shown in Fig. 3. Figure 4 shows the change of the numberof followers of the accounts that had increasing rate of thefollowers more than the average.
As for the change of the number of tweets, we naturally
can see the peak right after the day 3/11, but the patterns
seem to vary for the other parts. As for the change of thenumber of the followers, some accounts have increasing
number of followers while other accounts do not have suchrapid increasing number of followers. There seems to be thatthe accounts that already had many followers before the day
3/11 were likely to have more increasing rate of thefollowers after the day 3/11.

International Journal of Informatics Society, VOL.5, NO.3 (2013) 137-145

4.2 Quantitative Categorization of the
Accounts

139

To better understand what was going on after the generalview in 4.1, we categorized all the accounts into four groups
in terms of1) the number of tweet per day, andFigure 1: Change of the number of tweets for all the accounts

Figure 2: Change of the number of tweets for the 33 accounts

140

T. Inoue / Toward the operation guidelines for municipal social media account:
From the analysis of user’s behavior on Twitter in the Great East Japan Earthquake

Figure 3: Change of the number of followers for all the accounts

Figure 4: Change of the number of followers for the accounts that were above the average inincreasing rate of followers

International Journal of Informatics Society, VOL.5, NO.3 (2013) 137-145

Figure 5: Four groups according to quantitative
categorization of the accounts2) the increasing rate of the number of follower per day
after the date of the earthquake.
The average number of tweet per day was 7.5 and theaverage increasing rate of the number of follower per day
was 1.15%. Thus the account could be divided whether it
was more than 7.5 tweets or not, and whether it had morethan 1.15% increasing rate or not. As shown in Fig. 5, we

141

numbered each group from (1) to (4) that was same with thequadrant.
Figure 6 shows the graphs of the group (1) with many
tweets and high increasing rate of followers. Five accounts
belonged to this group. We see additional graphs besides thenumber of tweets and the number of the followers in the
figure. Those are the number of tweets per content category.
EQ-Communication, EQ-Citation, EQ-Announcement, EQPrimary Info and Non-EQ in the legend are the content
categories where EQ means earthquake. They are explainedlater in this paper. Figures 7 and 8 also have the same graphs
and legends. This group (1) gained rapid increasing numberof followers after the day 3/11.
Figure 7 shows the graphs of the group (2) with many
tweets and low increasing rate of followers. Four accounts
belonged to this group. Although this group had many
tweets but the change of the number of tweets was not very
noticeable.
The group (3) is one with few tweets and low increasingrate of followers. Twenty accounts belonged to this group.
Although most number of the accounts belonged to thisgroup, the group appears to be of inactive accounts. Thus
this group is not focused in this paper hereafter. Figure 8
shows the graphs of the group (4) with few tweets and highincreasing rate of followers. Five accounts belonged to thisgroup.

Figure 6: Group (1) with many tweets and high increasing rate of followers

142

T. Inoue / Toward the operation guidelines for municipal social media account:
From the analysis of user’s behavior on Twitter in the Great East Japan Earthquake

Figure 7: Group (2) with many tweets and low increasing rate of followers

Figure 8: Group (4) with few tweets and high increasing rate of followers

International Journal of Informatics Society, VOL.5, NO.3 (2013) 137-145

Figure 9: Tweet content of Group (1)Figure 11: Tweet content of Group (2)We might expect that many tweets draw many followers.
This was not true however. Certainly it seemed to be truethat few tweets usually did not draw much attention, as seenin the group (3). We also saw the cases where many tweetsdrew much attention as seen in the group (1). However at thesame time we saw the cases where many tweets did not draw
much attention as in the group (2), and the cases where few
tweets drew much attention as in the group (4).
Through this quantitative categorization, we could seeinteresting outline patterns of tweets and followers in thegraphs. But this is not enough to explain what causes highincreasing rate of followers.

4.3

Content of the Tweets

The content of the tweets were then investigated. The
tweets were categorized in the following five types.
a-1) Earthquake related: Communication with otheraccount (EQ-Communication).
This includes the reply using @ to communicate
with other accounts about the earthquake. The reply to otheraccounts is in this category even if “RT” (this means
retweet) was used. The tweet that used RT to make theinformation or the information exchange itself open can bein this category. Even if the tweet included new information,
it is in this category if its main purpose is for communication.

143

Figure 10: Tweet content of Group (4)a-2) Earthquake related: Citing existing information (EQCitation)
This includes the tweet that cites existing
information related to the earthquake. The retweet, and thetweet of a link to the related news are included in thiscategory. The tweet that includes cited information andappended new information can be in this category or in a-4)
category depending on the weight of both parts.
a-3) Earthquake related: Announcing to people (EQAnnouncement)This includes the tweet that is related to theearthquake and that can be taken as the announcement to theunspecified followers. Difference from a-4) category is that
this does not include new information, and often includes acall or request for action. A tweet of general caution forblackout is an example.
a-4) Earthquake related: Announcing primary information(EQ-Primary info)
This includes the tweet that provides theinformation where the tweeter is the primary source, newinformation in other words. The examples are the tweet ofprimary information when the official Web site is notrunning, and the tweet of notifying the official Web update
related to the earthquake.
b) Earthquake non-related (Non-EQ)
This includes the tweet whose content is not related
to the earthquake. For example, all the tweets before theearthquake belong to this category.
The process to determine these content categories was asfollows:
1) Two persons, a collaborator and the author, wereinvolved with the process.
2) The tentative categories and their criteria were
decided through the discussion over the collected
tweet data.
3) One account was selected from each of the fourgroups in Fig. 5 randomly as the sample accounts. The
tweets of those accounts from 3/11 to 3/17,
immediately after the earthquake when Twitter users
took rapid following actions, were categorizedaccording to the tentative categories and the criteria.
The categorization was independently conducted by
the two persons.

T. Inoue / Toward the operation guidelines for municipal social media account:
From the analysis of user’s behavior on Twitter in the Great East Japan Earthquake

1444) To check the two person’s agreement on
categorization, Cohen’s Kappa coefficient wascalculated.
Cohen’s Kappa = 0.861 for 1016 tweet items
5) After being confirmed that the Kappa was highenough, the categories and their criteria were
determined without change.
6) The rest of the tweets were categorized according to
the criteria by the collaborator.

4.4

Account Groups and Their Content

The content categories of the tweets in each account groupare also shown in Figs. 6-8. Clear contrast between thegroups was found. Many tweets in the group (1) and (4),which had high increasing rate of followers, seemed to be ina-4). Whereas many tweets in the group (2), which had low
increasing rate of followers, seemed to be in a-2).
To analyze the relation between the account groups andthe content categories more, the tweet content from 3/11 to
3/17, a week since the earthquake, was investigated in eachof the three groups. The contents of the group (1), (4), and(2) are shown in Figs. 9, 10, and 11 respectively.
What is common in the group (1) and the group (4) is thatthe most numbers, nearly 70%, of tweets were in a-4). In
contrast the most numbers of tweets in the group (2) were in
a-2).
From this analysis, we see that the accounts that tweetedprimary information were followed by many people.

5

OPERATION GUIDELINES

From the result of the previous section, we can draw
operation guidelines for a municipal Twitter account inemergency.
(1) First and foremost, provide primary information.
People want it.
Accounts with high increasing rate of followers mainly
provided a-4) category tweets regardless of the number oftweet.
(2) Immediate tweet is helpful.
This can be observed from Figures 6 and 8. The content
changed, and the number of tweets also changed after theearthquake in the group (1) and (4). The group (1) accounts
that had been relatively active before the earthquake
responded more immediately than other accounts, and were
followed by much more people.
(3) Communication can also be helpful.
The group (1) accounts were most followed. This can beconsidered they were most useful or they drew mostattention. What is different from other groups and is evendifferent from the group (4) is that they tweeted a-1) more
often.
(4) Citation can be tweeted, but not so many.
All accounts tweeted citation to some degree, but thegroup (2) which tweeted citation mainly did not get highincreasing rate of followers in spite of many tweets.
(5) Daily activity to have followers is significant.
The group that gained the most number of followers, andthe group that gained the most increasing rate of followers
was the group (1). Another difference of the group (1) fromother groups is that it held the most number of followers
before the earthquake. Having more followers means having
stronger dissemination ability, and this could result to draw
more people.

6

CONCLUDING REMARKSIn this paper, the use of Twitter accounts by municipalorganizations was focused. The user’s behavior on Twitterin the Great East Japan Earthquake was analyzed from bothquantitative and qualitative viewpoints in terms of thenumber of tweets and the tweeted content. Motivated reason
is because it is not clear how such social media accounts canbe used effectively in the emergency, although such social
media, Twitter especially, are encouraged to use by theJapanese government.
Through this research, it was revealed that there weretypes of behaviors of the municipal accounts and some were
useful and were followed by many people. Then the results
obtained by close investigation were organized as theoperation guidelines for a municipal Twitter account inemergency.
Although the research achieved this clear contribution,
there are issues to consider in the future. This analysis only
focused on the tweet itself. Other factors could affect theuser’s behavior. Examples are geographical factor andorganization factor. The account closer to the disaster areas
could be more followed. The account of the organization
whose activity is more related to the disaster could be more
followed. Those factors could be related to the number ofprimary information. Thus analysis from different
viewpoints can be conducted.
Another issue is credibility of tweet. Twitter among othersocial networking services suffers from identity verificationand fact verification. False rumor has been regarded as aproblem. It is possible that the user’s behavior reflected selfprotection from false rumors. The credibility of municipal
accounts is high, and this could push the number offollowers. The same behavior might partially explain why
primary information was evaluated higher than citation.
With this consideration, information from municipal accountis important, and primary information from municipal
account is even more important. Designing more crediblesocial medium can also be another direction from thisresearch.

ACKNOWLEGMENT
The author would like to thank Naoko Yoshimura for her
contribution to the data collection and the initial analysis.

REFERENCES[1] T. Tokuda, ``Crisis Responses to the Great East Japan
Earthquake: 11. Local Government Crisis Response
andInformation
Technologies,’’
Information
Processing Society of Japan Magazine, Vol.52, No.9,
pp.1082-1083 (2011).
[2] K. Wakita, ``Crisis Responses to the Great East Japan
Earthquake: 9. Smartphone Applications at the Time ofInternational Journal of Informatics Society, VOL.5, NO.3 (2013) 137-145

Crisis,’’ Information Processing Society of Japan
Magazine, Vol.52, No.9, pp.1078-1079 (2011).
[3] METI (Ministry of Economy, Trade and Industry),``On information broadcasting by governmental or
municipal organizations using social media,’’
http://www.meti.go.jp/press/2011/04/20110405005/201
10405005.html (accessed 2012-06-20).
[4] M. Mendoza, B. Poblete, and C. Castillo, ``Twitter
under crisis: Can We Trust What We RT?,’’ Proc. of
the 1st Workshop on Social Media Analytics, pp.71-79(2010).
[5] B. De Longueville, R. S. Smith, and G. Luraschi,``“OMG, from here, I can see the flames!”: A Use Caseof Mining Location Based Social Networks to Acquire
Spatio-temporal Data on Forest Fires,’’ Proc. of the2009 International Workshop on Location Based Social
Networks, pp.73-80 (2009).
[6] S. Vieweg, A. L. Hughes, K. Starbird, and L. Palen,``Microblogging During Two Natural Hazards Events:
What Twitter May Contribute to Situational
Awareness,’’ Proc. of the 28th international conferenceon Human factors in computing systems, pp.1079-1088(2010).
[7] Y. Qu, C. Huang, P. Zhang, and J. Zhang,``Microblogging After A Major Disaster in China: A
Case Study of the 2010 Yushu Earthquake,’’ Proc. of
the ACM 2011 conference on Computer supported
cooperative work, pp.25-34 (2011).
[8] M. Miyabe, E. Aramaki, and A. Miura, ``Use Trend
Analysis of Twitter after the Great East Japan
Earthquake,’’ IPSJ SIG Technical Report, Vol.2011GN-81, No.17, pp.1-7 (2011).
[9] A. Umejima, M. Miyabe, E. Aramaki, and A.
Nadamoto, ``Tendency of Rumor and Correction Retweet on the Twitter during Disasters,’’ IPSJ SIG
Technical Report, Vol.2011-DBS-152, No.4, pp.1-6(2011).
[10] A. Lubna, and R. Lucas, ``Tweeting Government:
A Case of Australian Government Use of Twitter,’’IEEE International Symposium on Dependable,
Autonomic and Secure Computing, pp.995-1001(2011).
[11] A. Kavanaugh, E. A. Fox, S. Sheetz, S. Yang, L. T.
Li, T. Whalen, D. Shoemaker, P. Natsev, and L. Xie,``Social Media Use by Government: From the Routine
to the Critical,’’ Proc. of the 12th Annual International
Digital Government Research Conference: Digital
Government Innovation in Challenging Times, pp.121130 (2011).
[12] METI (Ministry of Economy, Trade and Industry),
Govtter, http://govtter.openlabs.go.jp/ (accessed 201206-20).
[13] H. Tsujimura, ``Twitter API Programming,’’
Works Corporation, p.344 (2010).
[14] Ropross, Twilog, http://twilog.org/ (accessed 201206-20).
(Received October 23, 2012)(Revised February 12, 2013)

145

Tomoo Inoue is Associate
Professor of the Faculty of
Library, Information and Media
Science of University ofTsukuba. His research interests
include HCI, CSCW, and CSCL.
He
received
Ph.D.
inEngineering
from
Keio
University in 1998. He is arecipient of awards including Best Paper Award,
Activity Contribution Award and SIG ResearchAward from Information Processing Society of Japan(IPSJ). He has served a number of academic
committees, currently including IEICE SIG Human
Communication Science, IEICE SIG Multimedia on
Cooking and Eating Activities, VRSJ SIG
Cyberspace, IPSJ SIG Digital Contents Creation,
IPSJ SIG Groupware and Network Services, APSCECUMTEL SIG, and IEEE TC CSCWD.

146