Detecting Unknown or Fake User Accounts on Different Micro blogging and Social Media Networks

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Volume: 3 Issue: 2 April - 2018 4

Detecting Unknown or Fake User Accounts on Different

Micro blogging and Social Media Networks

CHAITALI KELKAR , ASHISH MEHROTRA, SHANTLING KAPSE, PRIYANKA SHIRSAT, Dr. MEENAKSHI THALOR chaitalikelkar1@gmail.com, ashish.mehrotra41@gmail.com, ss.kapse@gmail.com, pri123@gmail.com, thalor.meenakshi@gmail.com

Computer Department AISSMS IOIT, Pune.

ABSTRACT

The previous couple of years have witnessed the emergence associate degreed evolution of a spirited analysis stream on an outsized style of on-line Social Media Network (SMN)platforms. Recognizing anonymous, but identical users among multiple SMNs continues to be associate refractory downside. Clearly, cross-platform exploration might facilitate solve many problems in social computing in every theory and applications. Since public profiles are duplicated and easily impersonated by users with utterly totally different functions, most current user identification resolutions, that within the main specialize in text mining of users’ public profiles, unit of measurement fragile. Some studies have tried to match users supported things and temporal property of user content in addition as communication. However, the locations unit of measurement distributed among the bulk of SMNs, and communication is difficult to select out from the short sentences of leading SMNs like S in a {very} very Microblog and Twitter. Moreover, since on-line SMNs unit of measurement quite symmetrical , existing user identification schemes supported network structure are not effective. The real-world friend cycle is incredibly individual and as regards to no two users share a congruent friend cycle. Therefore, it is a ton of correct to use a relationship structure to research cross-platform SMNs. Since identical users tend to line up partial similar relationship structures in various SMNs, we tend to tend to planned the Friend Relationship-Based User Identification (FRUI)algorithm. FRUI calculates a match degree for all candidate User Matched Pairs (UMPs), and alone UMPs with prime ranks unit of measurement thought of as identical users. we tend to tend to jointly developed two propositions to spice up the efficiency of the rule. Results of intensive experiments demonstrate

that FRUI performs much better than current

network structure-based algorithms.

Keywords

Cross platform, Social Media Network, Anonymous Identical Users, Friend Relationship, User Identification

1.

INTRODUCTION

In the last decade, many varieties of social networking sites have emerged and contributed vastly to massive volumes of real-world information on social behaviors. Twitter 1, the most important microblog service, has quite 600 million users and produces upwards of 340 million tweets per day [1]. Sina Microblog2, the first Twitter-style Chinese microblogs web site, has a lot of than500 million accounts and generates run out one hundred million tweets per day.

Due to this diversity of on-line social media networks (SMNs), individuals tend to use completely different SMNs for various functions. as an example, RenRen 3, a Facebook-style however anonymous SMN, is employed in China for blogs, whereas Sina Mi-croblog is employed to share statuses. In alternative words, each existent SMN satisfies some user desires. In terms of SMN management, matching anonymous users across different SMN platforms will give integrated details on every user and inform corresponding laws, like targeting services provisions. In theory, the cross-platform explorations permit a bird’s-eye read of SMN user behaviors. However, nearly all recent SMN-based studies focus on one SMN platform, yielding incomplete information. So, this study verifies strategy of crossing multiple social media network platforms to color a comprehensive image of those behaviors.

Nonetheless, cross-platform analysis faces various challenges. With the expansion of SMN platforms on the net, the cross-platform approach has unified numerous SMN platforms to form richer data and a lot of complete SMNs for social computing tasks. SMN user’s kind the natural bridges for these SMN platforms. The first topic for cross-platform SMN analysis is user identification for various SMNs. Exploration of this subject lays a foundation for any cross-platform SMN analysis.

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Traditional Approach:

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identification applications. In contrast with current

algorithms, FRUI requires no control parameters.

3.

PREVIOUS WORK:

The diversity of online social media networks (SMNs), people tend to use different SMNs for different purposes. For instance, RenRen 3, a Facebook-style but autonomous SMN, is used in China for blogs, while Sina Microblog is used to share statuses (Fig.1). In other words, every existent SMN satisfies some user needs.

4.

PROPOSED SYSTEM

System Architecture

In terms of SMN management, matching

anonymous users across different SMN platforms will give integrated details on every user and inform corresponding laws, like targeting services provisions. In theory, the cross-platform explorations permit a bird’s-eye read of SMN user behaviors. However, nearly all recent SMN-based studies focus on one SMN platform, yielding incomplete information. So, this study verifies strategy of crossing multiple social media network platforms to color a comprehensive image of those behaviors.

Nonetheless, cross-platform analysis faces various challenges. With the expansion of SMN platforms on the net, the cross-platform approach has unified numerous SMN platforms to form richer data and a lot of complete SMNs for social computing tasks. SMN user’s kind the natural bridges for these SMN platforms. The first topic for cross-platform SMN analysis is user identification for various SMNs. Exploration of this subject lays a foundation for any cross-platform SMN analysis .

Figure 1 : System Desig

5.

MATHEMATICAL MODEL

Let S is the Whole System Consist of

S= {I, P, O}

I = Input.

I = {U, Q, D}

U = User

U = {u1,u2….un}

Q = Query Entered by user

Q = {q1, q2, q3…qn}

D = Dataset

P = Process:

Step1: Social network creation.

Step2: User will register to particular social network for

creating an account.

Step3: Admin will login to the system.

Admin will detect the anonymous user account by

using an following three technique.

a)Profile-Based User Identification

Several

studies

addressing

anonymous

user

identification have focused on public profile attributes,

including screen name, gender, birthday, city and

profile image A screen name is the publically required

profile feature in almost all SMNs.

b)Content-Based User Identification

Content-Based User Identification solutions attempt to

recognize users based on the times and locations that

users post content,as well as the writing style of the

content.

c)Network Structure-Based User Identification

Network structure-based studies on user identification

across multiple SMNs are used to recognize identical

users solely by user network structures usi g longitude

and latitude of the system at the time of uploadation of

post and identified users. As shown above,

network-based

user

identification

poses

several major

challenges, with few studies to build on.

Step4:Check detected user friend list same as both

social network or not.

Step5:System will detect an anonymous user

Step 6: The algorithm repeats steps 2 to 4 until certain

terminating conditions are fulfilled, such as a

pre-defined number of iterations.

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6.

RESULT

Figures below shows the results of our implementation.

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Figure 2: Profile Based Detection

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7.

CONCLUSION

This study addressed the matter of user identification across SMN platforms Associate in Nursingd offered an innovative answer. As a key facet of SMN, network structure is of overriding importance and helps resolve de-anonymization user identification tasks. Therefore, we tend to planned a homogenous net-work structure-based user identification answer. we tend to conjointly developed a unique friend relationship-based formula referred to as FRUI. to enhance the potency of FRUI, we tend to de-scribed 2 propositions and addressed the complexness. Finally, we tend to verified our formula in each artificial net-works and ground-truth networks.

Results of our empirical experiments reveal that net-work structure will accomplish vital user identifica-tion work. Our FRUI formula is straightforward, nonetheless economical, and performed far better than NS, the prevailing state-of-art network structure-based user identification answer. In eventualities once raw text knowledge is thin, incomplete, or arduous to get owing to privacy settings, FRUI is very suita-ble for cross-platform tasks. privacy settings, FRUI is very suita-ble for cross-platform tasks.

8.

ACKNOWLEDGMENTS

We would like to express gratitude to our project guide Prof. Meenakshi Thalor for her expert advice and encouragement throughout this difficult project,as well as

project coordinator Dr. K.S. Wagh and Head of Department Prof. S.N. Zaware. Without their continuous support and encouragement this project might not have been possible.

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REFERENCES

[1] Wikipedia, "Twitter, " http://en.wikipedia.org/wiki/Twitter. 2014.

[2] Xinhuanet, "Sina Microblog Achieves over 500 Million Users," http://news.xinhuanet.com/tech/2012-02/29/c_122769084.htm. 2014.

[3] D. Perito, C. Castelluccia, M.A. Kaafar, and P. Manils, "How unique and traceable are usernames?," Privacy Enhancing Technol-ogies(PETS’11), pp. 1-17, 2011.

[4] J. Liu, F. Zhang, X. Song, Y.I. Song, C.Y. Lin, and H.W. Hon, "What's in a name?: an unsupervised approach to link users across communities," Proc. of the 6thACM international conference on Web search and data mining(WDM’13), pp. 495-504, 2013.

[5] R. Zafarani and H. Liu, "Connecting corresponding identities across communities," Proc. of the 3rd International ICWSM Con-ference, pp. 354-357, 2009. [6] R. Zafarani and H. Liu, "Connecting users across

social media sites: a behavioral-modeling approach, " Proc. of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13), pp.41-49, 2013.

[7] A. Acquisti, R. Gross and F. Stutzman, "Privacy in the age of aug-mented reality," Proc. National Academy of Sciences, 2011.

[8] T. Iofciu, P. Fankhauser, F. Abel, and K. Bischoff, "Identifying users across social tagging systems,” Proc. of the 5th International AAAI Conference on Weblogs and Social Media, pp. 522-525, 2011.

[9] M. Motoyama and G. Varghese, "I seek you: searching and matching individuals in social networks," Proc. of the 11th inter-national workshop on Web Information and Data Management (WIDM’09), pp. 67-75, 2009.

Figure

Figure 1 : System Desig

Figure 1 :

System Desig p.2
Figure 1: FRUI Based Detection

Figure 1:

FRUI Based Detection p.3
Figure 3: Detected Anonymous User

Figure 3:

Detected Anonymous User p.4
Figure 2: Profile Based Detection

Figure 2:

Profile Based Detection p.4

References

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