Classification Methods for Spam Detection in Online Social Network

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 994
Classification Methods for Spam Detection In
Online Social Network
SUPRIYA RAMHARI MANWAR1, Prof. P.D. LAMBHATE2, Prof. J. S. PATIL3
1 ME Student, Department of Computer Engineering, Jayawantrao Sawant College of Engineering, Pune,
Maharashtra, India
2 Professor, Department of Computer and IT Engineering, Jayawantrao Sawant College of Engineering, Pune,
Maharashtra, India
3 Professor, Department of Computer Engineering, Jayawantrao Sawant College of Engineering, Pune,
Maharashtra, India
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Abstract - In the recent advanced society the online
social networking sites like Twitter, Facebook, LinkedIn are
very popular. Twitter, an online Social Networking site, is
one of the most visited sites. Lot of users communicates with
each other using Twitter. The rapidly growing social
network Twitter has been infiltrated by large amount of
spam. As Twitter spam is not similar to traditional spam,
such as email and blog spam, conventional spam filtering
methods are not appropriate and effective to detect it. Thus,
many researchers have proposed schemes to detect
spammers in Twitter, so need to identify spammers in
twitter.
Spam detection prototype system is proposed to identify
suspicious users and tweets on Twitter. The proposed
approach is to identify spam in Twitter using template,
content, user based features to analyze behavior of user.
Twitter API is used to get all details of twitter user and then
generate the template. This template generated is then
matched with predefined template. If suspicious behavior is
analyzed, the account is considered as spam. However in
case spam is not detected, the system collects ‘content based’
and ‘user based’ features from twitter account, by using the
‘feature matching technique’ to match features.
Algorithms used in the proposed system are supported by
machine learning, which is used to match features and
identify spam. Two Classification Algorithms, Naive Bayes
and Support Vector Machine, are used for providing better
accuracy and reducing execution time by the use of
Template Matching. Public Dataset is collected from
internet for providing training to Naive Bayes and Support
Vector Machine classifiers.
Key Words: Spam Detection, Twitter, Support Vector
Machine, Naive bayes.
1. INTRODUCTION
Recently the use of Social Network is increased
tremendously to share people’s views and ideas. Twitter is
the social networking site used for sharing information
about real world achievements. However nowadays we
have observed that many people are using Twitter to do
marketing and to spread spam massages in OSN (Online
social Network).
Spammers have various kinds of motivations to spam the
messages. For some people, the motivation can be
financial gain; which is very clear from the tweets related
to advertising a product or tweets by an online merchant
to link to his website. Many a times these sellers may not
be meticulous, a n d s o they are prepared to disturb
users by blocking their Twitter feed. Another k i n d o f
common type of spam is the tweets containing
pornographic material or information of pornographic
websites. In such scenarios our spam detection task
could be viewed as a content filtering task.
Twitter does not allow pornographic material in profile,
header or background images, but many accounts
ignore this rule. This disregard for the Terms of Service
could arguably be reason enough to find and remove such
accounts. Whilst such content is viewed as lawful by
some and some want to see it, it is many times a fascia
for malware; links contained may be unsafe, with the risk
of user’s computer being infected with viruses.
The proposed novel approach is to detect spam in OSN. I
have used Machine Learning Approach to classify given
account and recognize the spammers. In Machine learning
we need trained machines to predict the respective
result to show spammers. Machine learning is divided
into two parts:
Supervised Learning and Non-supervised Learning.
In Supervised Learning, we need to train the classifier. In
Unsupervised Learning, we do not need to educate the
classifier. However Supervised Learning gives better
accuracy as compared to Unsupervised Learning.
In this paper, a description of twitter is given to identify
the spam. In section II, literature survey of spam detection
is done. Section III shows the proposed framework design.
In section IV detailed description of classification process
is described. Section V describes the dataset and predicted
results. In section VI Graph are shown showing the
Volume: 04 Issue: 07 | July-2017 www.irjet.net p-ISSN: 2395-0072

benefits and accuracy of the proposed solution. Finally,
section VII gives the conclusion of the paper.
2. REVIEW OF LITERATURE
Paper [1], Spam is not as diverse as It Seems: Throttling
OSN Spam with Templates Underneath, states that in
online social network, spam is originated from our friends
and thus it reduces the joy of communication. Normally
spam is detected in text format. The system collects large
amounts of data from online social network and that data
is used for identifying spam. This identified spam is used
for generating template. Whenever new stream of
messages comes for identifying spam or not spam, those
generated templates are used for matching with stream of
messages, so it reduces execution time of identifying spam.
That implemented framework is called as Tangram.
Paper [2], Detecting and Characterizing Social Spam
Campaigns mentions that several online social networks
are detected in internet. For identifying spam in online
social networks, existing method uses the Facebook wall
post. Crawlers are used for collecting wall post in
particular Facebook user. Then this wall post filters and
finally collects wall post which contains the URLs. This
method differentiates wall post text and link which is
mentioned in the wall. This method collects group from
similar texture content and posts it including the same
destination URLs. Post Similarity graph clustering
algorithm is used to identify similarity between post and
URL. Based on this malicious user and post is identified.
Paper [3], WARNINGBIRD: Detecting Suspicious URLs in
Twitter Stream details about three modules, Data
Collection, Feature Extraction and Classification. Under
Data Collection, system collects tweets with URL by using
Twitter Streaming API which is publicly available for
getting data from twitter. In Feature Extraction, features
are extracted from existing data. URL redirects chain
length like feature collect system because attackers use
long URL redirect chain to make analysis difficult.
Suspicious URL on twitter is classified Based on the
feature.
Paper [4], Suspended Accounts in Retrospect: An
Analysis of Twitter Spam states that spam users
continuously send abuse data in online social network.
In this study, system first of all collects the 1.8 billion
account data which is spam and analyzes web services
like URL which contain abuse data. Based on the
collected data we identify given account is spam or not
spam.
Paper [5], Detecting spammers on social networks
mentions that system collects user information like
tweets, number of followers etc. This is done using
Weibo API which is used for crawling. Feature
extraction module uses two important features, Content
based and User based features. In Content based feature,
system identify number of posts and number of repost
per day. User based feature extracts tweet post date,
average number of messages and URL posted per day. Based
on this feature SVM classifies instance. This binary
classifier predicts whether user is spam or not spam.
Paper [6], Towards online spam filtering in social
networks mentions that Online Social Networks (OSNs)
are very much popular among Internet users. In case it is
handled by wrong people, they are also effective tools for
spreading spam campaigns. In this paper author present
an online spam filtering system that can be used real time
to inspect messages generated by users. The system can
be deployed as a component of the OSN platform. Author
proposes to rearrange spam messages into campaigns for
classification instead of examining them individually.
Although campaign identification is used for offline spam
analysis, author applies this technique to support the
online spam detection problem with sufficiently low
expenses. Accordingly, this system adopts a set of fresh
features that effectively distinguish spam campaigns. It
drops messages classified as “spam” before they reach the
recipients, thus protecting them from various kinds of
fraud. The system is evaluated using 187 million wall
posts collected from Facebook and 17 million tweets
collected from Twitter.
3. EXISTING SYSTEM
Existing system used user-based and content-based
features that are different between spammers and
legitimate users. Then, they use these features to facilitate
spam detection. Using the API methods provided by
Twitter, they crawled active Twitter users, their
followers/following information and their most recent 100
tweets. Then, we analyzed the collected dataset and
evaluated our detection scheme based on the suggested
user and content-based features. They show result by use of
classifiers.
In Existing System required more execution time for
identify spam in Twitter Data and that methods provide the
less Accuracy.
Disadvantages of Existing System
1.It required more computational time for running
classifier because while running they match training and
testing instances.
2.System degrades the accuracy because system uses the
classification only.
3.This application used in real time spam detection so it
must have to provide better performance.

4.In classiﬁcation, classiﬁer identify spam based on
training data. This approach not ability to identify new
type spam.
4. PROPOSED SYSTEM
Detailed description of the system is discussed in this
section.
System Overview
The aim of the proposed spam detection system is to
detect the spam in Twitter by providing proper
identification of spam in real time Twitter data. It
provides accurate and the fast spam detection. In Existing
System required more execution time for identify spam
in Twitter Data and that methods provide the less
Accuracy.
Figure 1. System Architecture
Module Description:
1) Data Collection: To fetch a data from twitter we need
access of twitter, access obtained by creating a twitter
Application. Whenever we create application we get four
access keys from twitter.
They are four required for integrate
twitter:
• Consumer Key
• Consumer Secret
• OAuth Access Token
• OAuth Access Token Secret
By using this key in Java program we are able to collect
user data. System collects the input as twitter data and
later use for template matching or classification using
SVM.
2) Template Matching: Template contains bag of words.
Given template matched with predefined template and
identify spam or not spam user. If not spam then later we
use twitter data for classification. If spam then given user
is considered as a spam.
3) Preprocess: The twitter contain noise. That will
decrease accuracy of the system so we need to
remove noise from the twitter data.
4) Feature Extraction: We collect user based feature and
content based feature from twitter data. User based
feature contains user name, profile image, account details
etc. Content based feature contains user tweets retweet
etc. Based on this feature we train and test the model and
identify spam using support vector machine and Naive
Bayes algorithm.
5) Classification: SVM classification is essentially a binary
(two-class) classification technique, which has to be
modified to handle the multiclass tasks in real world
situations. SVM and Naive Bayes classification uses
features of twitter data to classify. This classification is
uses trained twitter feature and classify testing twitter
feature and identify spam or not.
6) Template Generation: If Support Vector Machine and
Naive Bayes detected as spam then we generate template
and given template added into predefined template
5. CLASSIFICATION
Input: A Twitter Feature
Dataset:
We used public SMS Spam Collection dataset which is
available on internet. Dataset contains sentence with class
label. We train Naïve Bayes algorithm and assigned label
like ham and spam. Ham class label contains 4825
instances and spam class label contains 747 instances
based on this instance, system predicts the given tweet is
spam or not spam.
In stop word removal technique system uses the mallet
LDA Stop word dataset. Mallet LDA contains list of stop
words and that stop word compare with tweet and
remove words which is present in dataset.
Output: class label (spam or not spam)
Process of SVM:-
1) Compute Score of input vector:
2) Kernel function (Radical basis function):
3) Class y= -1 whenoutput ofscoring function is negative.
4) Class y = 1 when output of scoring function is positive.
Parameter Xi ith value of input vector Yi ith value of
class label aiis the coefficient associated with the i
th training dataset b -Scalar value.

Process of Naive Bayes Therom: -
Bayes theorem provides a way of calculating posterior
probability P(c|x) from P(c), P(x) and P(x|c). Look at the
equation below:
Algorithm for updated Naivebayes :
 xi includes the contextual information of the document
(the sparse array) and yi its class.
 N is the size of the training dataset.
 P(c—x) is the posterior probability of class (target)
given predictor (attribute).
 P(c) is the prior probability of class.
 P(x—c) is the likelihood which is the probability of
predictor given class.
 P(x) is the prior probability of predictor
6. RESULT AND ANALYSIS
We collect manually data using Twitter API and those data
used for feature selection and analyzing user account is
spam or not spam.
Twitter Spam Percentage Graph
We perform spam detection on Facebook’s twitter
account and then fetch the tweets in Facebook account.
Template matching to detect tweet spam or not spam.
Then calculate percentage of spams by using given
formula.
Percentage of spams=total no. of spam count / total no of
tweet * 100
Result Table:-
[1] Twitte
r Account
[2] Spam
Count
[3] Not
Spam
Count
[4] Total
Count
[5] Facebo
ok
[6] 459.0
[7] 1641.
0
[8] 2100.
0
[9] Gmail
[10] 252.0
[11]
[12] 1848.
0
[13] 2100.
0
[14] Linked
In
[15]
[16] 232
[17] 1596.
0
[18] 2100.
0
This table shows the output of spam detection, we analyze
three Twitter account like Facebook, Gmail and LinkedIn.
Gmail and LinkedIn accounts have less spam percentage
as compare to Facebook twitter account. If spam
percentage is less then that account is not spam.
Figure 2 shows the Facebook page in twitter how many
spam tweets identified. Red color shows the spam tweet
percentage and blue color shows the not spam tweet
percentage. We collect tweet from twitter and remove the
stop words from tweet and then apply naïve Bayes
classification.
Figure 2. Twitter Spam Percentage Spam
Accuracy Graph
Figure 3 shows the accuracy comparison with SVM and
updated naïve bayes. In previous system standard naïve
bayes gives 93.7 but we use combination of entropy and
naïve bayes which gives 97.4910394265233 accuracy.
SVM is not giving better accuracy.
For analyze accuracy we used Weka tool. Naive bayes give
97% accuracy on spam identification and Svm give 56%
accuracy.

Figure 3. Spam Detection Accuracy Result
Execution Time Graph
Figure 4 shows the execution time required for Tweet
collection, Stop word removal and classification. Tweet
collection required more time as compare to others
because it collects tweet from online twitter account and
speed totally depend on internet speed.
Stop word removal technique remove the stop words from
tweet and System compare tweet word with predefined
stop word dataset.
In Implementation we are use java language so we get
execution time in nanosecond by using System class. We
convert given nanosecond into second and plot the graph.
Figure 4. Spam Detection Time Graph
7. CONCLUSIONS
In this project we used template matching approach for
identify given tweet is spam or not. There are two main
factor of that project which is accuracy and execution time.
For providing more accuracy we are using updated naïve
bayes with the help of entropy and naïve bayes. For
providing less execution time we are store trained data in
main memory as well as choosing naïve bayes algorithm.
The updated naïve bayes performs less process so that will
reduce the processing time and improving performance of
the system.
8. FUTURE SCOPE10
In future we are detecting spam on other online social
networks like Facebook, Google+ and Linkedin etc as well
as we also detects collusion and Sybil attack in twitter
accounts. After that we give permission from twitter to
remove spam accounts and tweets from twitter or other
online social network.
9. ACKNOWLEDGEMENT
It is with the profound sense of gratitude that I
acknowledge the constant help and encouragement from
our guide Prof. P. D. Lambhate mam and Co-guide Prof.
J. S. Patil mam and HOD Sir, Computer Engineering
Department, also PG co-ordinator Sir and Principal Sir
JSCOE, Hadapsar, for their sterling efforts, amenable
assistance and inspiration in all my work. They have given
in depth knowledge and enlightened me on this work.
10. REFERENCES
[1] H. Gao et al., Spam aint as diverse as it seems:
Throttling OSN spam with templates underneath, in Proc.
ACSAC, 2014, pp. 7685. Gao, J. Hu, C. Wilson, Z. Li, Y.
Chen, and B. Y. Zhao.
[2] HongyuGao NorthwesternUniversity Evanston,IL,USA
hygao@u.northwestern.edu, JunHu HuazhongUniv. ofSci.
&Tech andNorthwesternUniversity
junehu1210@gmail.com Detecting and Characterizing
Social Spam Campaigns.
[3] S. Lee and J. Kim, WarningBird: Detecting suspicious
URLs in Twitter stream, in Proc. NDSS, 2012, pp. 113.
[4] K. Thomas, C. Grier, V. Paxson, and D. Song,
Suspended accounts in retrospect: An analysis of Twitter
spam, in Proc. IMC, 2011, pp. 243258.
[5] Xianghan Zheng, Zhipeng Zeng, Zheyi Chen, Yuanlong
Yu, ChunmingRong,Detecting spammers on social
Networks, Neuro-computing,
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[6]Hongyu GaoNorthwestern University,Evanston,
ILUSA,hygao@u.northwestern.edu,Yan Chen
Northwestern University Evanston, IL, USA
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Filtering in Social Networks.
[7] K. Thomas, C. Grier, J. Ma, V. Paxson, and D.
Song, Design and evaluation of a real-time URL spam

filtering service, in Proc. IEEE Symp. SP, May 2011, pp.
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[8] J. Mottl, Twitter acknowledges 23 million active users
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BIOGRAPHY
Ms. Supriya Ramhari Manwar is
currently pursuing M.E.
Computer From Jayawantrao
Sawant College of Enginnering,
Savitribai Phule Pune University
Pune, Maharashtra, India. She
received her B.E. Computer
Degree from Amaravati
University. Her area of interest is
Data Mining.
Author’s
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