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Reseach Article

Spam Detection in Social Media Networks: A Data Mining Approach

by Harshal S. Multani, Amrita Sinh Marod, Vinita Pillai, Vishal Gaware
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 9
Year of Publication: 2015
Authors: Harshal S. Multani, Amrita Sinh Marod, Vinita Pillai, Vishal Gaware

Harshal S. Multani, Amrita Sinh Marod, Vinita Pillai, Vishal Gaware . Spam Detection in Social Media Networks: A Data Mining Approach. International Journal of Computer Applications. 115, 9 ( April 2015), 9-12. DOI=10.5120/20178-2385

@article{ 10.5120/20178-2385,
author = { Harshal S. Multani, Amrita Sinh Marod, Vinita Pillai, Vishal Gaware },
title = { Spam Detection in Social Media Networks: A Data Mining Approach },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 9 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-12 },
numpages = {9},
url = { },
doi = { 10.5120/20178-2385 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:54:20.880693+05:30
%A Harshal S. Multani
%A Amrita Sinh Marod
%A Vinita Pillai
%A Vishal Gaware
%T Spam Detection in Social Media Networks: A Data Mining Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 9
%P 9-12
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

The ubiquitous use of social media has generated unparalleled amounts of social data. Data may be – text, numbers or facts that are computable by a computer. A particular data is absolutely useless until and unless converted into some useful information. It is necessary to analyze this massive amount of data and extracting useful information from it. There are more active internet users on social networks than search engines. Social media networks provide an easily accessible platform for users who wish to share information with others. Information can be spread across social networks quickly and effectively, hence have now become susceptible to different types of undesired and malicious spammer/hacker actions. Therefore, there is a pivotal need for security in social media and industry. In this demo, a scalable and online social media spam detection system for social network security using TF-IDF algorithm is proposed.

  1. Xin Jin, Cindy Xide Lin, Jiawei Han, JieboLuo - A Data Mining-based Spam Detection System for Social Media Networks
  2. GAD: General Activity Detection for Fast Clustering on Large Data ?Xin Jin, Sangkyum Kim, JiaweiHan ,Liangliang Cao , Zhijun Yin ,University of Illinois at Urbana-Champaign
  3. Using a Data Mining Approach: Spam Detection on Facebook- M. Soiraya, S. Thanalerdmongkol, C. Chantrapornchai , Department of Computing, Faculty of Science , Silpakorn University, Thailand, 73000
  4. C. Shekar, S. Wakade, K. J. Liszka, and C. -C. Chan. Mining pharmaceutical spam from twitter. In ISDA, pages 813–817, 2010.
  5. K-Means Clustering Scheme for Enhanced Spam Detection-Nadir Omer FadlElssied and Othman Ibrahim Faculty of Computing, University Technology Malaysia, 81310, Skudai, Johor Bahru, Malaysia AlgerafSharq Technical College, Khartoum, Sudan
  6. Xiao-Li, C. , L. Pei-Yu, Z. Zhen-Fang and Q. Ye, 2009. A method of spam filtering based on weighted support vector machines. Proceeding of the IEEE International Symposium on IT in Medicine and Education, pp: 947-950.
  7. A Survey of Data Mining Techniques for Social Media Analysis - Mariam Adedoyin-Olowe, Mohamed MedhatGaber, Frederic Stahl
  8. J. S. Kong, B. A. Rezaei, N. Sarshar, V. P. Roychowdhury, and P. O. Boykin. Collaborative spam filtering using e-mail networks. IEEE Computer, 39(8):67–73, 2006.
  9. K. Yoshida, F. Adachi, T. Washio, H. Motoda, T. Homma, A. Nakashima, H. Fujikawa, and K. Yamazaki. Density-based spam detector. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 486–493, 2004.
  10. B. Byun, C. -H. Lee, S. Webb, and C. Pu. A discriminative classifier learning approach to image modeling and spam image identification. In CEAS, 2007.
  11. F. Benevenuto, T. Rodrigues, V. Almeida, J. M. Almeida, C. Zhang, and K. W. Ross. Identifying video spammers in online social networks. In AIRWeb, pages 45–52, 2008.
  12. S. Webb, J. Caverlee, and C. Pu. Introducing the webb spam corpus: Using email spam to identify web spam automatically. In Proceeding of the Third Conference on Email and Anti-Spam (CEAS), 2006
  13. B. Markines, C. Cattuto, and F. Menczer. Social spam detection. In AIRWeb, pages 41–48, 2009.
  14. K. Lee, J. Caverlee, and S. Webb. Uncovering social spammers: social honeypots + machine learning. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '10, pages 435–442, 2010.
  15. S. Webb, J. Caverlee, and C. Pu. Social honeypots: Making friends with a spammer near you. In Proceeding of the Fifth Conference on Email and `Anti-Spam (CEAS), 2008.
  16. Gerard Salton and Christopher Buckley, Department of Computer Science, Cornell University, Ithaca, NY 14853, USA . Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management Vol. 24, No. 5, pp. 513-523,1988.
  17. Juan Ramos, Department of Computer Science, Rutgers University, 23515 BPO Way, Piscataway, NJ, 08855: Using TF-IDF to Determine Word Relevance in Document Queries
  18. Willett, P. (2006) The Porter stemming algorithm: then and now. Program:electronic library and information systems, 40 (3). pp. 219-223.
Index Terms

Computer Science
Information Sciences


Spam Social media networks Security TF-IDF.