A Survey on Spam Detection Methodologies in Social Networking Sites

Abstract
Authors
Keywords
Conclusion
References

Conventional media, such as television or newspapers, essentially transmits information in one direction. Social media is a two-way form of communication that allows users to interact with the information being transmitted. Social media encompasses a wide variety of online content, from social networking sites like Facebook . online social networks are becoming popular among internet users. The internet users spend more amount of time on popular networking sites like Facebook, Twitter, google+ etc. Huge information available on these sites attracts the spammers who misuse the valuable information on these sites. Spammers send unwanted messages, share malicious links, develop malicious apps and sometimes create fake accounts. A lot of research has been done to detect spam on social networking sites. In this paper we have reviewed different research papers on spam detection. Our study provides techniques used, dataset and accuracy of various spam detection methodologies.

Published In : IJCSN Journal Volume 6, Issue 4

Date of Publication : August 2017

Pages : 483-488

Figures :07

Tables : 01

K Subba Reddy : Research Scholar, Computer Science and Engineering Acharya Nagarjuna University, Guntur , AP, India.

Dr E Srinivasa Reddy : Principal, Anucet, Computer Science and Engineering Acharya Nagarjuna University, Guntur, Ap, India.

Social Media, Facebook, Twitter, Spammer, Internet

The results of present study revealed that, it is possible to utilize mobile networks and near-infrared technologies to create a system to monitor HBO and HB in the human brain and tissues where it achieved similar results to fMRI. Further work will be required to ensure the system provides reliable results for animals. There are infinite possibilities to integrate these technologies and create cheaper solutions to replace expensive existing solutions by simply utilizing that already exists and synthesizing improvement. However, there are also several challenges in the field of computer science engineering and biomedical physics that need to be addressed to make the system perform as intended.

[1] De Wang, Danesh Irani, and Calton Pu. A Social- Spam Detection Framework. CEAS 2011 - Eighth annual Collaboration, Electronic messaging, Anti- Abuse and Spam Conference Sept. 1-2, 2011. [2] Benjamin Markines,Ciro Cattuto,Filippo Menczer. Social Spam Detection. AIRWeb ’09, April 21, 2009 [3] Enhua Tan1, Lei Guo2, Songqing Chen3, Xiaodong Zhang2, and Yihong (Eric) Zhao UNIK: Unsupervised Social Network Spam Detection [4] Kyumin Lee, James Caverlee, Steve Webb:Uncovering Social Spammers: Social Honeypots + Machine Learning SIGIR’10, July 19– 23, 2010, ACM [5] 5.Xin Jin, Cindy Xide Lin, Jiebo Luo, Jiawei Han, SocialSpamGuard: A Data MiningBased Spam Detection System for Social Media NetworksThe 37th International Conference on Very Large Data Bases,August 29th September 3rd 2011,Proceedings of the VLDB Endowment, Vol. 4, No. 12 [6] Xueying Zhang, Xianghan Zheng, A Novel Method for Spammer Detection in Social Networks, IEEE- 2015 [7] Yin Zhuy, Xiao Wang, Erheng Zhongy, Nanthan N. Liuy, He Li, Qiang Yang: Discovering Spammers in Social Networks, Association for the Advancement of Artificial Intelligence (www.aaai.org).,2012 [8] Jyotika varma, Dr sanjeev Dhawan: detection of spam in social networks using clustered k-nearest neighbour,international journal of advanced research in computer science and software engineering,ijarcsse,volume 5,issue 3,march 2015 [9] Gianluca stringhini,Christopher kruegel,Giovanni vigna,detecting spammeres on social networks:ACM- 2010 [10] Faraz ahmed,Muhammad abulaish: An MCL Based Approach for spam profile detection in online social networks.IEEE 11th international conferene on trust,security and privacy in computing and communications-2012 [11] 11.Chengo cao,james caverlee: detecting spam URLs in social media via behavioural analysis, springer international publishing pp 703-714,2015 [12] 12 .Saini Jacob soman, Dr s murugappan; detecting malicious tweets in trending topics using clustering and classification, ieee international conference on recent trends in information technology -2014 [13] 13. Fabricio benevenuto, Gabriel magno, tiago rodrigues,and virgilio almedia: detecting spammers on twitter-ceas electronic messaging, antiabuse and spam conferene july 13-14,2010 [14] 14.Sajid Yousuf Bhat1, Muhammad Abulaish, Abdulrahman A. Mirza:Spammer Classification using Ensemble Methods over Structural SocialNetwork Features, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) [15] 15.Hailu Xu,Weiqing sun,Ahmad javaid: Efficient spam detection across online social networks,IEEE- 2015 [16] 16.Arushi gupta,rishabh kaushal: Improving spam detection in online social networks-IEEE-2015 [17] 17.M soirayan, S Thanalerdmongkol,c chantrapornchai :using a data mining approach :spam detection on facebook, International Journal of Computer Applications (0975 – 8887) Volume 58– No.13, November 2012 [18] 18. Krishna Chaitanya T, HariGopal Ponnapalli, Dylan Herts and Juan Pablo:: Analysis and detection of modern spam techniques on social networking sites 2012 Third International Conference on Services in Emerging Markets IEEE [19] 19. Xianghan Zheng a,b, ZhipengZeng a,b, ZheyiChen c, YuanlongYu a,b,n, ChunmingRong: Detecting spammers on social networks, Neurocomputing159(2015)27–34 [20] 20.De wang :Analysis and detection of low quality information in social networks, IEEE-2015.