Detection and Minimization Influence of Rumor in Social Network

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 757
Detection and Minimization Influence of Rumor in Social Network
Surekha Rawale1, Sapana Birkure2, Kajal Hiray3 , Prof. Deepti Deshmukh4
1,2,3 B.E. Students, Dept. of Computer Engineering, D. Y. Patil Institute of Engineering and Technology, Ambi,
Pune University, Maharashtra, India
4 Assistant Professor, Dept. of Computer Engineering, D. Y. Patil Institute of Engineering and Technology, Ambi,
Pune University, Maharashtra, India
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Abstract - With the fast development of big scale on-line
social networks, on-line datasharingisbecomingomnipresent
daily. Numerous info is propagating through on-line social
networks similarly as every the positive and negative.
Throughout this paper, we tend to tend to focus on the
negative data problems just like the on-line rumours. Rumor
block may well be a significant drawback in large-scale social
networks. Malicious rumours might causechaosinsocietyand
sought to be blocked as soon as potential once being detected.
during this paper, we tend to propose a model of dynamic
rumor influence reduction with user expertise (DRIMUX).Our
goal is to cut back the influence of the rumor (i.e., the number
of users that have accepted and sent the rumor) by block an
exact set of nodes. A dynamic Ising propagation model
considering every the worldwide quality and individual
attraction of the rumor is given supported realistic state of
affairs. To boot, altogether completely different from existing
problems with influence reduction, we tend to tend to require
into thought the constraint of user experience utility.
Specifically, each node is assigned a tolerance time threshold.
If the block time of each user exceeds that threshold, theutility
of the network will decrease. Underneath this constraint, we
tend to tend to then formulate draw back as a network
abstract thought drawback with survival theory, and propose
solutions supported most probability principle. Experiments
area unit implemented supported large-scale world networks
and validate the effectiveness of our methodology.
Key Words: Social network, Rumor Influence
Minimization, rumor blocking strategies, survival
theory.
With the speedy development and rising quality of large-
scale social networks like Twitter, Facebook etc., many in
numerable individualsarea unitreadytobecomefriends[10]
and share every kind of knowledgewithoneanother.On-line
social network analysis has additionally attracted growing
interest among researchers.[11],[12] On onehand,theseon-
line social platforms offer nice convenience to the diffusion
of positive info like new ideas, innovations, and hot topics.
On the opposite hand, however, they will become a channel
for the spreading of malicious rumours or information [1],
[15], and [16]. As an example,someindividualscouldposton
social networks a rumor concerning associate degree
approaching earthquake, which can cause chaos among the
group and thus could hinder the conventional public order.
During this case, it's necessary to discover the rumor Source
and delete connected messages, whichcanbeenoughtostop
the rumor from any spreading. However, in bound extreme
circumstances like terrorist on-line attack, it might be
necessary to disable or block connected Social Network (SN)
accounts to avoid serious negative influences. For instance,
in 2016, the families of 3 out of the forty 9 victims from the
metropolis cabaret shooting incident filed a causa against
Twitter, Facebook and Google for providing “material
support” to the coercion organization of the Islamic State of
Republic of Iraq and Asian nation (ISIS)[17].These
companies then took measures to dam connected accounts,
delete relevant posts and fan pages on their social network
platforms to stop the ISIS from spreading malicious info. to
boot, Facebook etv. even have issued relevant security
policies and standards to assert the authority to dam
accounts of users once they square measure against rules or
in danger [18]. without doubt, malicious rumors ought to be
stopped as presently as potential once detectedinorderthat
their negative influence will be reduced. Mostoftheprevious
works studied the matter of increasing the influence of
positive info through social networks. quick approximation
ways were additionally planned to influence maximization
drawback. In distinction, the negative influence attention,
however still there are consistent efforts on minimization
Problem has gained a lot of less planning effective ways for
obstruction malicious rumours and minimizing the negative
influence.
2. LITERATURE SURVE
PAPER(1): DRIMUX: Dynamic Rumor Influence
Minimization with User Experience in Social
Networks(2016)
In this paper, we have a tendency to specialize in the
negative info issues like the web rumors. Rumorobstruction
could be a significant issue in large-scale social networks.
Malicious rumors may cause chaos in society and therefore
got to be blocked as shortly as attainable once being
detected. during this paper, we have a tendencytoproposea
model of dynamic rumor influence reduction with user
expertise (DRIMUX). Our goal is to attenuate theinfluenceof
the rumor (i.e., the amount of users that have accepted and
sent the rumor) by obstruction a precise set of nodes
1. INTRODUCTION

PAPER (2): Maximizing Acceptance Probability for Active
Friending in Online Social Networks (2013)
In this paper, we have a tendency to advocate a
recommendation support for active friending, wherever a
user actively specifies a friending target. To the most
effective of our data, a recommendation designed to supply
steerage for a user to consistently approach his friending
target has not been explored for existing on-line social
networking services. to maximise the likelihood that the
friending target would settle for a call for participation from
the user, we have a tendency to formulate a replacement
optimisation downside, namely, Acceptance likelihood
Maximization (APM), and develop a polynomial time rule,
known as SelectiveinvitewithTreeandIn-NodeAggregation
(SITINA), to seek out the best resolution.wehavea tendency
to implement a full of life friending service with SITINA on
Facebook to validate our plan. Our user study and
experimental resultsreveal thatSITINAoutperformsmanual
choice and therefore the baseline approach in resolution
quality with efficiency.
PAPER (3): Limiting the Spread of Misinformation in Social
Networks (2011)
In paper developed four malicious applications, and
evaluated Andromaly ability to detect new malware
based on samples of known malware. They evaluated
several combinations of anomaly detection algorithms,
feature selection method and the number of top features in
order to find the combination that yields the best
performance in detecting new malware on Android.
Empirical results suggest that the proposed framework is
effective in detecting malware on mobile devices in general
and on Android in particular.
PAPER(4): Efficient Influence Maximization in Social
Networks (2009)
In this paper, we have a tendency to study the economical
influence maximization from 2 complementary directions.
One is to enhance the first greedy formula and its
improvement to more scale back its period of time, and also
the second is to propose new degree discountheuristicsthat
improves influence unfold. we have a tendency to measure
Our algorithms by experiments on 2 giant educational
collaboration graphs obtained from the net deposit
information arXiv.org.
PAPER (5): A Fast Approximation for Influence
Maximization in Large Social Networks (2014)
This paper deals with a completely unique analysis work a
couple of new economical approximation algorithmic
program for influence maximization, that was introduced to
maximise the good thing about infectious agent promoting.
For potency, we tend to devise 2 of exploiting the 2-hop
influence unfold which is that the influence unfold on nodes
inside 2-hops removed from nodes in a very seedset.Firstly,
we tend to propose a brand new greedy methodologyforthe
influence maximization drawback exploitation the 2-hop
influence unfold. Secondly, to hurry up the new greedy
methodology, we tend to devise a good manner of removing
uncalled-for nodes for influence maximization Based on
optimum seed’s native influence heuristics.
3. EXISTING SYSTEM
Most of the previous works studied the matter of
maximizing the influence of positive data through social
networks..In distinction, the negative influence diminution
drawback has gained a lot of less attention, however still
there are consistent efforts oncomingupwith effectiveways
for block malicious rumors and minimizing the negative
influence. Kimura et al. Studied the matter of minimizingthe
propagation of malicious rumors by block a restricted range
of links in a very social network. They provided2completely
different definitions of contamination degree and planned
corresponding optimisation algorithms. Fan et al.
investigated the smallest amount price rumor block
drawback in social networks. They introduced the
conception of “protectors” and check outtopick a borderline
range of them to limit the dangerous influence of rumors by
triggering a protection cascade against the rumor cascade.
However, there square measure some limitations in those
works.
Algorithm:1 Greedy Algorithm
Different from the greedy blockingalgorithm,whichisa type
of static blocking algorithm, we propose a dynamic rumor
blocking algorithm aiming to incrementally block the
selected nodes instead of blocking them at once. Inthatcase,
the blocking strategy is split into several rounds and each
round can be regarded as a greedy algorithm. Thus, how to
choose the number of rounds is also very important for the
algorithm. In the following part, we will elaborate on the
algorithm design and how we choose the specific
parameters. From the probabilistic perspective, we seek to
formulate the likelihood of inactive nodes becoming
activated in every round of rumor blocking.
Correspondingly, the likelihood
function is given by
Correspondingly, the objective function is

Then, the greedy algorithm is presented as below:
Input: Initial Edge matrix A0
Initialization: VB = 0;
for i = 1 to K do
u = arg max [f (t1|s (t0); Ai-1) - f (t1 | s (t0); Ai-1 ʋ)]
Ai: = Ai-1u,
VB = VB U {u}
end for
Output: VB.
Mathematical Model of Existing System
System S as a whole can be defined with the following main
components.
S= {I, O, P, s, e, U, Uf, Ad};
S=System
s=Initial State
e=Final State
U= user
Uf=Set of user friends
Ad=admin
Input {I} = {Input1, Input2}
Where,
Input1=Text
Input2=Images
Procedures {P}= {Up,Sp,Ubloack,Rdetect}
Where,
Up=upload post.
Sp=Share Post.
Ubloack= Block user who sent or shared rumor text and
images.
Rdetect=Detect rumor text and images.
Output {O} = {Output1, Output2}
Where,
Output1=detecting rumor texts & images
Output2=block user who sent or shared rumor text and
images
s= {initially system will be in a state where user are not
enrolled, Only admin of system.}
e= {users are enrolled and successfully post or share text or
images & admin detect and rumor text and images and also
block user who sent or shared rumor text and images }
Result of Existing System:
Existing system Detect rumor post and text and block user
who sent rumor test or image for long time so they may quit
social network. And not delete rumor post.
4. PROPOSE SYSTEM
We propose a rumor propagation model taking under
consideration the subsequent 3 elements: initial, the
worldwide quality of the rumor over the whole social
network, i.e., the final topic dynamics. Second, the attraction
dynamics of the rumor to a possible spreader, i.e., the
individual tendency to forward the rumor to its neighbours.
Third, the acceptance chance of the rumor recipients. In our
model, galvanized by the Ising model, we have a tendency to
mix all 3 factors along to propose a cooperative rumor
propagation chance. In our rumor interference ways, we
have a tendency to think about the influence of interference
time to user expertise in universe social networks.therefore
we have a tendency to propose a interference time
constraint into the standard rumor influence diminution
objective perform. in this case, our technique optimizes the
rumor interference strategy while not sacrificing the web
user expertise.
we have a tendency to use survival theory to investigate the
chance of nodes turning into activated or infected by the
rumor before a time threshold that is setbytheuserexpertise
constraint. Then we have a tendency to propose each greedy
and dynamic interference algorithms mistreatment the most
chance principle.
Advantages of Proposed System
1) Efficacy of our system is better than existing
System.
2) Our system block user who shares rumor posts for
particular period of time
Result of Proposed System:
Proposed system Detect rumor post and text and block user
who sent rumor test or image for short period of time so

they not quit social network. And delete rumor post to avoid
chaos among the crowd.
Algorithm:2 Dynamic Blocking Algorithm
Different from the greedy blockingalgorithm,whichisa type
of static blocking algorithm, we propose a dynamic rumor
blocking algorithm aiming to incrementally block the
selected nodes instead of blocking them at once. Inthatcase,
the blocking strategy is split into several rounds and each
round can be regarded as a greedy algorithm. Thus, how to
choose the number of rounds is also very important for the
algorithm.
Input: Initial Edge matrix A0
Initialization: VB(t) = 0.
for j = 1 to n do
for i = 1 to kj do
Δf = f (tj |s (tj-1); Ai-1) – f (tj |s (tj-1); Ai-1ʋ),
u = arg max {Δf},
Ai: = Ai-1u,
VB(tj) = VB(tj) U {u}.
end for
end for
Output: VB(t).
Algorithm:3 K-means Algorithm
k-means is one of the simplest unsupervised learning
algorithms that solve the well known clustering problem.
The procedure follows a simple and easy way to classify a
given data set through a certain numberof clusters(assume
k clusters) fixed apriori. The main idea is to define k
centers, one for each cluster. These centers should be
placed in a cunning way because of different location
causes different result. So, the better choice is to place
them as much as possible far away from each other. The
next step is to take each point belonging to a given data set
and associate it to the nearest center. When no point is
pending, the first step is completed and an early group age
is done. At this point we need to re-calculatek newcentroids
as barycenter of the clusters resulting from the previous
step. After we have these k new centroids, a new binding has
to be done between the same data set points and the
nearest new center. A loop has been generated. As a resultof
this loop we may notice that the k centers change their
location step by step until no more changes are done or in
other words centers do not move any more.
Mathematical Model of Existing System
System S as a whole can be defined with the following main
components.
S= {I, O, P, s, e, U, Uf, Ad};
S=System
s=Initial State
e=Final State
U= user
Uf=Set of user friends
Ad=admin
Input {I} = {Input1, Input2}
Where,
Input1=Text
Input2=Images
Procedures {P}= {Up,Sp,Ubloack,Rdetect, Rdelete }
Where,
Up=upload post.
Sp=Share Post.
Ubloack= Block user who sent or shared rumor text and
images.
Rdetect=Detect rumor text and images.
Rdelete= Delete rumor text and images
Output {O} = {Output1, Output2, Output3}
Where,
Output1=detecting rumor texts & images
Output2=delete rumor texts & images
Output3=block user who sent or shared rumor text and
images
s= {initially system will be in a state where user are not
enrolled, Only admin of system.}
e= {users are enrolled and successfully post or share text
or images & admin detect and delete rumor text and
images and also block user who sent or shared rumor text
and images }
5. CONCLUSION
In this paper, we tend to investigate the rumor obstruction
downside in social networks. we tend to propose the
dynamic rumor influence reduction with user expertise
model to formulate the matter. A dynamic rumor diffusion
model incorporating each world rumor quality and
individual tendency is conferred supported the Ising model.
Then we tend to introduce the thought of user expertise
utility and propose a changed version of utility perform to
live the connection between the utility and obstructiontime.
After that, we tend to use the survival theory to investigate
the probability of nodes obtaining activated beneath the
constraint of user expertise utility.Greedy algorithmic rule

and a dynamic obstruction algorithmic rule area unit
projected to unravel the optimization downside supported
totally different nodes choice methods. Experiments
enforced on planet social networks show the efficaciousness
of our methodology.
6. ACKNOWLEDGEMENT
This work is supported by Prof. Deepti Deshmukh (D Y
Patil Institute of Engineering & Technology, Ambi)
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