23rd Workshop on Information Technology and Systems (WITS2013),
Milan, Italy, December 2013

User Roles in the Time of Crisis: A Social Media
Analysis

Nargis Pervin, Remy Cazabet, Hideaki Takeda, Fujio Toriumi and Anindya Datta
Abstract
Dynamics of information propagation in Twitter has been studied in the context of retweet
practices. In order to build the network of information flow, either the activity network or thefollower network has been considered. However, because of unstandardized retweet practices
the bias in diffusion analysis is not avoidable in prior approaches. By combining both activity
network and follower network, we introduce the concept of “Information Diffusion Impact”(IDI) on network. With a Twitter dataset during the Great Eastern Japan Earthquake wecharacterize important user roles in information propagation in the time of crisis and discuss
evolution of user roles over time.

1 Introduction
Microblogging services such as Twitter turns out to be the most popular service by far which candisseminate up-to-the-minute information very quickly. This massive information resource has
proven to be useful for event detection [10], political uprising [1], and organizing people in thetime of crisis [6, 9]. However, little is known about how information is propagated and people
behave during natural calamity.
The principal factor of information diffusion in Twitter is the so-called act of retweeting, whichallows users to share information published by others. Diffusion of information in Twitter has
already been studied by several researchers in the context of trending topic detection [10], modelingretweetability of a tweet [2, 11], and adoption cascades [5]. But the different roles played by users
in this diffusion of information needs further investigation.
Chu et al.[3] have proposed a system to classify Twitter accounts into human, bots, and cyborgs(human assisted bots or bot assisted humans) computing entropy of inter-tweet delays. Tinati etal. [12] have proposed a model to classify several types of users, that they call Idea Starters,
Amplifiers, Curators, Commentators, and Viewers. However, as we will explain in Section 3, theirretweet network may suffer from a strong bias. Furthermore, the metrics used to identify the rolesof the users are based on a specific model, called ‘Topology Of Influence’. On the contrary, inthis paper, we propose definitions of user roles directly grounded on the impact the users have on
the network. Our goal is in two folds: first we analyze the retweet dataset along with the follower
network to characterize different user roles in the context of information diffusion. Secondly, we
study the change of user roles in the time of crisis.
Herein, we present a realistic approach of finding the retweet chain from tweet content. Upon
analysing the retweet chain we classify user roles in three categories, namely, “idea starter”, “amplifier”, and “transmitter” in the context of information diffusion in the network. Later, we analyze
1the change of user roles in the time of crisis using a Twitter dataset of the Great East Japan Earthquake in 2011.
This paper is organized as follows. Section 2 presents the dataset description used in this study.
Section 3 describes the user role classification followed by experimental settings and results in
Section 4 and Section 5 respectively. Finally, Section 6 concludes our findings.

2 Dataset Description and Preparation
2.1 Dataset Description
Tweet Data: We used a Twitter dataset collected during the earthquake in 2011 described thoroughly in [13]. The dataset covers a period of 20 days (from 5th March, 2011 to 24th March, 2011),
and consists of 362,435,649 tweets posted by 2,711,473 users in Japan. This dataset is remarkableby its completeness: the authors have checked that 80% to 90% of all published tweets by theseusers were present in this dataset.
Fig. 1(a) shows the retweet count for a period of 20 days normalized to cut off daily variations.
The first two major peaks represent the two big earthquakes on 11th and 12th March as reported in[14]. After the disaster, retweet count progressively returns to its normal average values.
Follower Network Data: In Twitter, follower network depicts the social relationship among theusers. Follower information has been collected by crawling Twitter API in May, 2013 for the active
users who have been mentioned more than 20 times in 20 days. Follower network dataset consistsof 300,104 users and 73,446,260 relationships information. Degree distribution has been shownin Fig. 1(b) by plotting cumulative fraction of users against the number of followers/followees ofuser.
Cumulative fraction of users

10
8
6
4
2
0
6th Mrach
7th March
8th March
9th March
10th March
10th March
11th March
12th March
13th March
14th March
15th March
16th March
17th March
18th March
19th March
20th March
21st March
21st March
22nd March
23rd March
24th March

Normalized Retweet Count

Followers
Followees

1

12

0.8

0.6

0.4

0.2

0
1

Time (per hour)

10

100

1000

10000

100000

Number of followers/followees(a) Tweet Distribution over days (Normalized)(b) Cumulative fraction of users by degree

Figure 1: Retweet distribution and degree distribution

2.2 Dataset Preparation
In this work, we have investigated the activity network and the static follower network of theTwitter users simultaneously. In Twitter, the retweet functionality allows users to share information
with their friends and followers, generating a network of retweeters. Here, this retweet network is
considered as activity network. In our case, to analyze the information diffusion for each tweet,
we were interested in the retweet sequence of each tweet, which we name as retweet chain.
2How to find Retweet Chain: In most recent work, particularly the previous work proposing aclassification of user’s roles [12], the diffusion of information is directly extracted from the content
of the tweets. For instance, if a tweet published by user u1 is composed of the following pattern:
RT @u0 tweet then one considers that the information diffused directly from u0 to u1 . Retweet
chains are identified by tweets containing several references, i.e RT @u1 RT @u0 tweet, or consecutive citations, such as u1 posting the retweet : RT @u0 tweet followed by u2 posting RT @u1
tweet.
However, in reality, after one step of citation, this has two important biases:• users tend to keep only the original author of the tweet, and not intermediates, in particular
to meet the 140 character limit of Twitter. Even when using the official Retweet functionof Twitter, only the initial poster is kept, which will strongly increase the number of direct
retweet and in turn the apparent role of the original poster in the diffusion of information.
• users frequently retweet after seeing a tweet several times, as it has been shown in [8]. As aresult the user cited as source might not be fully representative of the information diffusion.
In this work, to characterise the diffusion of information, we therefore adopt a combination ofboth the follower network and the retweeter information from tweet. A retweet chain is simply
defined as the sequence of all published tweets containing the original content, ordered by their
publication time. To study the information flow, we combine this information considering that,each time a user publish a tweet, all his followers can see the information. We can therefore know
by whom the user might have been informed, independently of the user who appears as source inthe tweet itself.

3 User Role Classification
We classify user roles in the light of information propagation through retweeting. By analyzing
retweet chain the users are classified into three categories, “idea starter”, “amplifier”, and “transmitter”.
• Idea starters are the users who are able to launch new ideas which will spread broadly in the
network. They are the users whose ideas will reach many.
• Amplifiers are users who do not publish interesting content by themselves, but have thepotential to diffuse information published by others to many new people.
• Transmitters are users who act as bridges between several communities in the network. If
an idea starter publish an interesting tweet in a given community of the network, amplifiers
will spread this tweet in the same community, but transmitters are necessary to reach othercommunities, which in turn will result in transmission of the information more broadly.
We base our user role definitions on the concept of Information Diffusion Impact (IDI), namely
for a user, the number of times he made one user aware of one piece of information. Therefore,making 10 people aware of one information and making one person aware of 10 different pieces
of information result in the same IDI value. This notion is very important, as it allows to compare3the impact of different roles. For each user, we can compute a value of IDI for each behavior(idea starter, amplifier, and transmitter), which represents the impact of this user on the diffusionof information: how many people were impacted by his publication of popular tweets? How many
people became aware of a tweet by his action of retweeting? And how many people could accessthe information because the user transmitted it to another community? These values are therefore
comparable.
Notations used in this paper has been listed in Table 1.
NotationNtNuCif ollowerCi (u)RC(t)
order(t, u)Meaning
Number of new people aware of tweet t
Number of new people made aware by user u
Community i
Follower set of user u in community Ci
Retweet Chain of tweet t
Position of user u in the retweet chain of tweet t

Table 1: Notation Table
Idea Starter: Idea starter can be conceptualized as the one who creates the original information.
Idea starters are important as their tweets are retweeted broadly in the network depending on theimportance of the content. Using u’s follower information, for each user u in the retweet chain of
tweet t (RC(t)), we compute the number of new people (Nu ) u makes aware of. The total numberof people ∑(N t ) in the network aware of the tweet t is given byt
N = u∈RC(t) Nu .
Here, N t is the number of different users made aware of the tweet, which is the impact of idea
starter u for tweet t. Hence, the overall impact of u as an idea starter is defined by,{∑
1, if order(t, u) = 1.
IdeaStarter(u) =σN t , σ =0, otherwise.
t
It is worthy to note that, to become a good idea starter it is not necessary to be followed by many
people.
Amplifier: Amplifiers are considered as the individuals who share other’s information and makemany people aware of it. They are important as they are followed by many users and as a result,amplifiers make a large fraction of users aware of the information. Unlike idea starter, impact ofamplifiers depend only on their immediate neighbors. For each tweet t, u participated but was not
the idea starter, we compute the number of new people u makes aware of denoted by N t .
{∑1, if order(t, u) > 1.
Amplif ier(u) =σN t , σ =0, otherwise.
t
We should note that this value is usually less than the number of followers of u, as some of
his followers have already been made aware of the tweet. Therefore, users who appear early inthe retweet chain, or who tend to inform users following few people will naturally have higher
amplifier scores.
Transmitter: It is now accepted that most social networks have a strong community structure [4].
The Twitter follower network is no exception and its analysis reveals clearly defined modules. In
4this paper, we used the Fast OSLOM algorithm [7] to detect communities in our follower network
because it is fast and can find overlapping communities. The algorithm found 8 communities in
our follower network with an average size of 44668 nodes per community. By manual investigation, we found obvious meaning for some communities, such as a community of foreigners and acommunity of users related to night life (disc jokeys, hip-hop celebrities, etc.).
We observed that communities play an important rolein the diffusion of information. Most tweets are diffused
only in a fraction of the communities. For instance, let
us consider that a tweet is diffused in a community if it isretweeted by its users more than half as much as expected
Idea Starterby picking users randomly. Then only 12% of tweets
Ampliﬁer
are diffused in the foreigner community of 43,375 users,
while 64% of tweets are diffused in another communityof comparable size. This indicates that tweets are not easily transmitted to the foreigner community. In contrary,
more than 95% of tweets are considered transmitted toTransmitterthe largest community of size 150,235. We observed that
only 38% of tweets that diffuse in the foreigners’ comFigure 2: Retweet network for a popular tweet
munity also diffuse in the largest community. This suggests that transmitters are needed to spread the information into or outside of this community.
Therefore, we identify transmitters as users who spread tweets initially stuck in a community A
to another community B. We consider that a tweet is stuck in a community if the first 20 retweets
are in the same community. Therefore, the first user from a different community B to retweet isconsidered as transmitter to B if he again gets retweeted by other people from B.
The impact of a transmitter for one tweet is simply the number of people who gain access tothe information by his retweet. More formally, the effective number of users informed about thetransmission of tweet t in community Cj is the summation of the number of followers of retweetersin community Cj .
{∑1, if order(t, uk ) > order(t, u).
T ransmitterCt j (u) = |f ollowerCj (u)|+σ|f ollowerCj (uk )|, σ =0, otherwise.
u ∈C
k

j

where order(t, u) is the position of user u in RC(t) and f ollowerCj (u) represents the numberof followers of user u in community Cj .
If a user belongs to several communities, he can be transmitter to different communities fora single tweet. If he does not transmit t to a community Cj , then T ransmitterCt j (u) = 0.
∑
T ransmittert (u) = j T ransmitterCt j (u)Hence, overall transmitterscore of u for all tweet he is transmitter can be given by∑
T ransmitter(u) = t T ranmittert (u)Figure 2 shows the retweet network corresponding to a popular tweet, where each node represents a user and an edge v → u exists if v follows u and order(t, v) > order(t, u) in RC(t). Nodecolor represents the community it belongs and size of node is indicator of number of follower ofthe node in the network. Using our metric we identify the idea-starter, amplifiers, and transmittersin the retweet chain. One can note that the idea-starter is not followed by many people, as the size5

of the node is comparatively small. Amplifier is the one with many followers and well-connectedin the network. On the other hand transmitter is the node from a different community where hediffuses the information.

4 Experimental Settings
Using Twitter dataset described in Section 2.1, we find the top 10,000 popular tweets, where
popularity is measured by number of times it has been retweeted. For all these 10,000 tweets,
retweet chain has been formed, which is basically the chain of users in chronological order of theirretweet of the original post. Our tweet dataset (5th March-24th March) have been divided into
three time windows, pre-crisis (5th March-10th March), during-crisis(11th March-17th March),and post-crisis(18th March-24th March). Using the follower information idea starter, amplifier,and transmitter IDI value of each user in all three time windows have been computed.

5 Experimental Results
5.1 Evolution of User Roles over TimeIn different tweets, one user might have different roles, idea starter/amplifier/transmitter. We havemeasured the individual impact for each role. In Figure 3 and Figure 4 we have computed thepercentage of users who retained and disappeared as idea-starter and amplifier respectively in threetime windows. Figure 3(a) shows the overall distribution of the idea starter in the three timewindows. 69% of the total idea starters were emerged only during the earthquake and 7% of the
popular idea starters remained popular after the event also. To understand the transition of theidea-starter from one time-window to another, we analysed the proportion of the idea-starters in
pre-event time-window who retained in other time-windows. From Figure 3(b), we see that out of
49 users in pre-event time-window, only 12 retained during the event. After the event it was only
7. Also, a large number (349) of idea-starters emerged during the event and 38 of them retained
and 53 new users emerged after the event.
Similar analysis has been carried for amplifiers in Figure 4(a) and Figure 4(b). Overall distribution of the amplifier in Figure 4(a) shows that the number of new amplifiers only during theevent is very high (95%) and only 4% of the amplifiers who emerged during the event continued tocontribute after the event also. Figure 4(b) shows that popular amplifiers in pre-event time-window(13) tends to be popular during the event (11) and after the event (9), though a high number of amplifiers appeared only during the event (7400). Comparing Figure 3 and Figure 4, a large number ofidea-starters and amplifiers in the post-event time-window were from during-event time-window.

5.2 Association of User RolesIn Figure 5, we plotted (in log scale) Idea Starter Impact against Amplifier Impact for each user
for three time-windows. We have divided the region into four quadrants as shown in Figure 5(d)).
We have plotted only the users for whom sum of idea-starter impact and amplifier impact is at least
100,000, which means that overall the user has impacted 100,000 users in the network. In pre-event
time-window (Figure 5(a)), number of high-impact idea starters (in quadrant 4) is comparatively
larger than high-impact amplifiers (quadrant 2). Number of super-users in pre-event time-window
6

7%

1%

1000

8%

Number of idea-starter

1%
14%pre-eventduring-event
post-event
pre+during
during+post
pre+post#pre-event users

100#during-event users#post-event users
10

all time
1pre-event during-event post-event
69%

Time period(a) In all time windows(b) Pre-and during event window

Figure 3: Distribution of role retention as idea starter in pre-, during- and post-event time window
10000

1% 4%
Number of amplifiers

pre-eventduring-event
post-event
pre+during
during+post

1000#pre-event users

100#during-event users#post-event users
10

pre+post
95%

all time

1pre-event(a) In all time windows

during-event post-event
Time period(b) Pre-and during event window

Figure 4: Distribution of role retention as amplifier in pre-, during- and post-event time window
is comparatively lower than other time-windows. In during-event time window (Figure 5(b)), there
is a strong increase in the number of users in all quadrants and number of super-users are maximum
during the event, contributing a lot in launching important ideas and spreading to others in the
network. Also we have observed that many idea-starters started behaving as amplifier during thedisaster. For instance, the user ‘earthquake jp’ is a bot which posts information on earthquake andseismic intensity. In the pre-event time window it acted only as good idea starter. However, during
the event it started retweeting others’ tweets and became potential amplifier, as commonly referred
as cyborg in the literature [3]. Interestingly, after the event (Figure 5(c)) it became again a bot.
Unlike ‘earthquake jp’, user ‘nhk pr’ was idea-starter as well as amplifier in all time. Particularly
during the disaster, he became very popular both as idea starter and amplifier and also remains as
potential idea starter and amplifier after the event. Interestingly, in the post-event time-window,
many users were observed with high impact in dual roles and some new users emerged as potential
idea starter and amplifier after the event and the number of super-users increases compared topre-event time-window.
We do not present similar experiments for transmitter as getting high transmitter value is rarer
than other two metrics. However, a comparison of top 100 idea-starters, amplifiers, and transmitters is carried out, which reveals that 21 users were listed in both top-idea starter and top-amplifier,
7 were listed in both amplifiers and transmitters and 1 was in top-idea starters and transmitters. The
popular celebrity with Twitter id ‘ayu 19980408’ was there in all three top-lists which indicates
her potential influence in the network.

7

Pure Amplifier(b) During-event window
High Impact
Super User

100000

AmplifierHigh Impact
Average Users
Idea Starter

0

Amplifier Impact(a) Pre-event Time window

Pure Idea Starter
0(c) Post-event window

100000Idea Starter Impact(d) Division of users in four quadrants

Figure 5: Idea-starter vs. Amplifier impact in pre-, during- and post-event time window

5.3 Transmitter’s Topology
According to raw IDI values, transmitters do not have an impact as high as the two other roles.
However, many transmitters had a strong impact on the diffusion of information with 15 users
having a overall transmitter score above 100,000 and 538 users with a score above 10,000.
We can identify two categories of transmitters. The first category corresponds to users who are
frequent transmitters to small communities (Table 3). They were transmitters for several dozensof tweets, but the community to which they transmitted is not very large, resulting in relatively
low scores. On the contrary, some of the users (Table 2) with the highest transmitter scores aretransmitters in less than 5 tweets. But they are transmitting an information from small communities
to the largest ones. Therefore, a tweet which could have reached only a fraction of all users withouttransmission, reach most of the network after transmission. For example, one tweet was launchedby someone from the night life community, asking for help for refugees in a town affected by thetsunami. After being retweeted by some people from the same community, it was transmitted tothe other communities and became widely retweeted. The impact of the transmission is therefore
very high in this case.
Name

Transmission #transmission Informed comIDI

polyjamlythiriana
rt report

177332156351150909munity size

2
2
2

Name

Transmission #transmission Informed comIDI

150235117193150235

satos cafe bar 57945
ayu 19980408 50153
bigkottakromac 106834

Table 2: Top Transmitters by IDI

munity size

102
102
58

11462
12177
20824

Table 3: Top Transmitters by number of transmission

85.4 IDI of User Role and Number of Followers

1.E+07
1.E+078.E+06
6.E+06
4.E+06
2.E+06
0.E+00Transmitter IDI
Amplifier IDI
Idea starter IDI
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100

IDI Impact

We investigated the Pearson correlation among overall idea-starter, amplifier, and transmitter impact of each user with that of their number of followers. We find that number of followers is notcorrelated with idea-starter impact (correlation =0.2827), amplifier impact (correlation =0.4352),
and transmitter impact (correlation =0.0273).
Figure 6 shows the contrast of 100 top-followed users with idea starter, amplifier, and transmitter impact. One can note that amongst the top-followed users, the roles are very different andthey have very different IDI impact. Hence, metrics like number of followers cannot determine theuser-roles or even the global IDI of the user in the network.

Rank of users by number of followers

Figure 6: Comparison of number of followers with IDI impact of three roles

6 Conclusion
Information diffusion in Twitter happens mainly by the act of retweeting. In most recent works
only tweet content is analyzed to build the diffusion network. However, common unstandardized
retweet practices introduce bias in the analysis, which can be mitigated by embedding follower
network information along with the tweet content. In this study we systematically constructed
and analyzed retweet chains to define user’s role based on the concept of Information Diffusion
Impact (IDI). In the context of information diffusion we identify three important user roles, namely“idea starter”, “amplifier”, and “transmitter”. While idea-starters are good at launching interestingideas, amplifiers are needed to diffuse the information broadly in the network, and transmitters
are important to reach different community. Analyzing Twitter dataset of the Great East Japan
earthquake data, we observe the change of these user roles in three different time-window, before,
during, and after the disaster. Our findings show that popular idea starters and amplifiers tend to
be popular during the disaster, but a large number of new users also emerge as good idea-starters
and amplifiers. We also identified super-users with high potential to both amplify and launch new
ideas.

Acknowledgement
We thank Genta Kaneyama (Cookpad Inc.) for assistance in collecting data from Twitter.

9

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