Journal of Software Engineering and Applications, 2013, 6, 102-105
doi:10.4236/jsea.2013.63b022 Published Online March 2013 (http://www.scirp.org/journal/jsea)
The Simulation of Rumor’S Spreading and Controlling in
Micro-Blog Users’ Network*
Peng Chen1, Nan Gao2
1Security Department, Chinese People’s Public Security University, Beijing, China; 2Computer Science and Engineering Department,
South University of Carolina, Unite States.
Received 2013
ABSTRACT
In this work, a rumor’s spreading and controlling in a directed Micro-blog user network being consisted with 580 000
nodes are simulated. By defining some authority nodes that release anti-rumor information as the prevention strategy,
the effect of the nodes’ role in network on rumor’s suppression is studied. The findings show that rumor will be spread
out fast and reach a stable level within limited steps. The suppression of rumor is more predominated by the intervening
opportunity, the earlier the intervention strategy was implemented, the better the rumor’s controlling could be achieved.
The controlling effect is less relevant with the role of the authority nodes in network.
Keywords: Component; Rumor; Micro-Blog; Network; Simulation
1. Introduction
As a fast developed production in the web, Micro-blog is
playing a more and more important role in Internet net-
work. Each day billions of blogs are created, commented
and transferred by the users. The Micro-blog is so wel-
comed for its highly convenience in information spread-
ing especially in some disaster emergency responses the
people could distribute the instant messages on the web
so that public can quickly know what happened in the
field and then make decisions of what to do to cope with
the problems. However, accompanying the advantages of
the Micro-blog also comes out some negative aspects.
For example, if someone releases misleading information
or rumor on Micro-blog then many people will be influ-
enced through the connections between the users.
As a matter of fact, the study about rumor of informa-
tion spreading in the social network was researched by
some people previously. For example, Sudbury, Zanette
and Zhou made simulation about opinion or news
spreading in complex social network and they found that
as most as 80% of nodes could receive the news in ran-
dom connected structure [1-3]. Xu investigated how ru-
mor spread through blog and his study indicated that in-
fection rate has significant influences on rumor’s spread-
ing [4]. Additionally, by constructing a framework of
identification algorithm, Liu studied detection of opinion
leaders in Micro-blog and he found that most opinion
leaders are topic dependent [5]. These previous theoreti-
cal and simulation work concentrate mainly on the rela-
tionship between rumor spreading and topology of the
social network, but little was done to study the prevent-
ing method or suppression strategy. So, in this work, by
collecting data of Micro-blog users’ attributes we con-
structed their social relationship network and made
simulations to study the rumor’s spreading and control-
ling with the aim of finding out an effective prevention
strategy.
2. Model and Data
The model SIR is the one being used broadly in rumor
spreading study. By definition it has three subgroups:
spreader, ignorant, stifler. When a spreader contacts with
an ignorant the latter becomes a spreader with probability
α, and when a spreader contacts another spreader or a
stifler the initiating spreader becomes a stifler at a rate β.
But considering the rumor’s spreading in Micro-blog is
such a quick process that many spreaders are not going to
become stiflers until they know the truth, we assume that
spreaders maintain the infection state for a long time.
Therefore, the spreading process is shown in the follow-
ing way:
S(i)+I(j)→S(i)+S(j) (1)
While the process is totally dominated by parameter α.
But for the spreaders changing into stiflers, we assume
they will become the stiflers 100% when they receive the
information released from the authority node. Thus this
*This paper is sponsored by Natural Science Project of Chinese Peo-
p
le’s Public Security University (No.2011LG12)

The Simulation of Rumor’S Spreading and Controlling in Micro-Blog Users’ Network
103
process could be shown in this way:
S(i)+ST(j)→ST(i)+ST(j) (2)
So in the work two information transferring processes
are included, one is rumor spreading originated randomly
from a node and the other is the truth spreading origi-
nated from an authority node. We study the rumor pre-
vention strategy by examining the interaction between
two processes. If the truth spreading is over the rumor
spreading we call it success prevention.
The social relationship data between the users was
collected from SINA website which is the largest Mi-
cro-blog server in China currently. A program was coded
and run at background to download the user’s personal
information automatically. This work started at
01/02/2012 and lasted to 29/02/2012 and more than 580
000 users’ recordings were collected. In the data each
user has an ID number to represent his identity.
In Micro-blog network the relationship between any
pair of nodes are unequal because not all nodes who fol-
low the others with interest can receive equal treatment.
So we call the one who following others with interest the
‘fan’. The relationships between two nodes are demon-
strated in Figure 1. From the graph it can be known that
when two nodes are fans to each other, the connection is
non-directed. However, if only one node is the fan to the
other the connection is directed. So using this relation-
ship information a social network between Micro-blog
users is constructed.
3. Definition
A sample of 50 000 user’s private information was col-
lected to determine the infection probability α. Their
visible attributes include gender, age, education and reg-
istration. The details of these attributes are listed below:
 Gender: male/female
 Registration: yes/no
 Age: lower than 20/20-30/30-40/larger than 40
 Education: elementary school/junior middle school/
senior middle school/university
The percentage of the users by the attributes is calcu-
lated and the result is shown in Table 1. From the find-
ings we can know that predominant users are male, non-
(a)
(b)
Figure 1. The relationship between two nodes.
registered, higher educated and aged between 20 and 30.
So with this distribution of statistic we define a kind of
conservative index to represent the users’ responding
attitudes towards the rumor.
Hypothesis 1: females are more conservative than
males, so females’ conservative index is 1.0, males is 0.5.
Hypothesis 2: the users who registered has better credit
than those don’t, so registration ones’ index is 1.0,
non-registration ones’ is 0.5.
Hypothesis 3: the people having better education
background are inclined to be more conservative, so the
index for people who had been in college is 1.0, senior
middle school is 0.75, junior middle school is 0.5, and
elementary school is 0.25.
Hypothesis 4: aged people are more conservative than
younger ones, so for people who aged more than 40 the
index is 1.0, 30-40 is 0.75, 20-30 is 0.5, and lower than
20 is 0.25.
With the proportion of people by their attributes, the
infection probability is calculated with formula (3):
α=ΣAipai+ΣEipei+ΣGipgi+ΣRipri (3)
Where Ai, Ei, Gi, Ri are the conservative indexes for
age, education, gender and registration attributes. pai, pei,
pgi and pri are percentages of the users belonging to dif-
ferent attribute group (see Tab le 1). Based on the formu-
lation the calculated infection probability is 0.35.
4. Simulation and Findings
As the relationships between two nodes are unequal, the
Micro-blog user network is a directed connected struc-
ture. The node following others with interest is a fan, so
its connectivity is out-degree. While for the node that is
being followed, its connectivity is in-degree. The distri-
bution of in-degree and out-degree to the nodes are cal-
culated and the relationship between their frequency and
scale are demonstrated in Figure 2. It can be seen that
the out-degree shows a linear trend in log-log plot, while
the in-degree shows a non-linear one.
The nodes’ in-degree is more important in information
transferring than out-degree because the in-degree indi-
cates that the node has so many fans so it could influence
its neighbor nodes directly by releasing information. In
contrast, out-degree only measures the ability of the node
receiving information from the others. In this case, a
Table 1. Percentage Ofusers by Attributes
Gender Aged Registration Education
Male
Female
72.7%
27.3%
< 20
20-30
30-40
> 40
13.4%
73.1%
11.8%
1.7%
Yes
No
20%
80%
ES
JS
SS
C
84.3%
10.5%
3.9%
1.3%
ES- Elementary school; JS- Junior middle school; SS- Senior middle school;
C- College

The Simulation of Rumor’S Spreading and Controlling in Micro-Blog Users’ Network
104
node with maximum fans was selected as the source of
the rumor and the spreading process was simulated. The
results are displayed in Figure 3 and each data was the
average of 50 repeated simulations. From the picture it
can be known that for the network that is consisting with
nearly 600 000 nodes the count of infected nodes quickly
reach a stable level within 20 time steps, or in other
words, the infected nodes reach a maximum level (16
496 nodes, about 2.8% in total, ) within limited steps.
(a)
(b)
Figure 2. The relationship between node connectivity and
frequency in Micro-blog user network. (a) in-degree; (b)
out-degree.
Figure 3. The variation of infected nodes along the timeline.
As a way to prevent the rumor spreading, we introduce
the prevention strategy into the simulation. We hypothe-
size there is an authority node which is playing the role
of the information monitoring. Once upon a time a rumor
is spreading is discovered in network it will instantly
release the truth to combat with the rumor. The infected
nodes contacting with the truth will become an ignorant
node and never be infected by rumor again. Initially, an
authority node with a certain number of fans was selected
and rumor’s spreading and truth diffusion process are
simulated. The results are shown in Figure 4. To inves-
tigate the effect of the suppression we implement the
prevention strategy at different opportunity. It can be
seen from the graph that rumor spreading is suppressed
successfully, and the earlier the intervention was imple-
mented the better the rumor spreading could be stopped.
The advantage of earlier intervention is not only effec-
tively shortening the rumor spreading period but also
significantly reduce the infected nodes.
Finally, we further investigate the significance of au-
thority node on rumor prevention. We defined 5 authority
nodes with different number of fans that representing
their different status. The more fans the node owning the
more important the nodes plays in the network. Then
simulations were made and results are demonstrated in
Figure 5. The results showed that no matter how fans
count changed the nodes infected by rumor did not vary
too much. Combined with the results in Figure 4 we
could find that the significance of authority node in net-
work has little influencing on rumor’s controlling, the
intervening opportunity is more predominant.
5. Conclusion
In this work, we simulate rumor’s spreading and control-
ling in Micro-blog user network by using a revised SIR
model. By collecting user’s social relationship and pri-
vacy information from online website the topology of the
network and spreading rules were defined. The findings
of the research demonstrate that in a Micro-blog user
Figure 4. The suppression to the rumor by implementing
prevention strategy at different opportunities

The Simulation of Rumor’S Spreading and Controlling in Micro-Blog Users’ Network
105
Figure 5. The suppression to the rumor by intervention
from the nodes with different in-degree connectivity.
network in which the nodes are directed connected the
rumor could quickly spread out and reach a top level
within limited steps. Implementing a prevention strategy
that releasing truth from an authority node is an effective
way to suppress the rumor spreading, but the intervening
opportunity is a very significant factor determining the
rumor’s controlling. The earlier the intervention was im-
plemented, the better the rumor could be suppressed.
Additionally the influence of the authority nodes with
different number of fans on rumor’s spreading and con-
trolling are studied and the results show that the signifi-
cance of the authority node in network has little rela-
tionship with the controlling effect. This study provides a
clue that however the authority node’s role in network,
supervising the information spreading in Micro-blogs
and making intervention in time is an effective way to
rumor’s suppression.
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