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Using a Data Mining Approach: Spam Detection on Facebook
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 58 - Number 13 |
| Year of Publication: 2012 |
| Authors: M. Soiraya, S. Thanalerdmongkol, C. Chantrapornchai |
10.5120/9343-3660
|
M. Soiraya, S. Thanalerdmongkol, C. Chantrapornchai . Using a Data Mining Approach: Spam Detection on Facebook. International Journal of Computer Applications. 58, 13 ( November 2012), 27-32. DOI=10.5120/9343-3660
Abstract
In this work, we present a social network spam detection application based on texts. Particularly, we tested on the Facebook spam. We develop an application to test the prototype of Facebook spam detection. The features for checking spams are the number of keywords, the average number of words, the text length, the number of links. The data mining model using the decision tree J48 is created using Weka [1]. The methodology can be extended to include other attributes. The prototype application demonstrates the real use of the Facebook application.
References
- "Weka 3: Data Mining Software in Java," University of Waikato, [Online]. Available: http://www. cs. waikato. ac. nz/ml/weka/. [Accessed 25 June 2012].
- X. Amatriain, A. Jaimes, N. Oliver and J. Pujol, "Data Mining Methods for Recommender Systems," in Recommender Systems Handbook, F. Ricci, Ed. , Springer Science+Business Media, 2011, pp. 39-71.
- Brian, "Five main methods of detecting patterns in data mining," [Online]. Available: http://legallysociable. com/2012/04/05/five-main-methods-of-detecting-patterns-in-data-mining/. [Accessed 30 June 2012].
- P. Hayati and V. Potdar, "Evaluation of spam detection and prevention frameworks for email and image spam: a state of art," in Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services (iiWAS), 2008.
- "Mail-SeCure Image-Based Spam Treatment," 2009.
- Y. Sawaya and Y. Miyake, "A Study of Spam Mail Detection System before. Receiving the Message Body," in Joint Workshop on Information Security Cryptography and Information Security Conference System, 2009.
- S. Krasser, Y. Tang, J. Gould, D. Alperovitch, P. Judge and S. Krasser, "Identifying Image Spam based on Header and File Properties using C4. 5 Decision Trees and Support Vector Machine Learning," in IAW 2007, 2007.
- Z. Wang, W. Josephson and Q. L. M. Charikar, "Filtering Image Spam with Near-Duplicate Detection," in CEAS, 2007.
- N. Spirin and J. Han, "Survey on Web Spam Detection:Principles and Algorithms," 2012.
- A. Benczúr, I. Bíró, K. Csalogány and T. Sarlós, "Web spam detection via commercial intent analysis," Alberta, Canada,,, 2007.
- L. Becchetti, C. Castillo, D. Donato and R. a. L. S. Baeza-YATES, "Link analysis for Web spam detection," ACM Transactions on the Web (TWEB), vol. 2, no. 1, February 2008.
- K. Suttirut and C. Phongpensri, "Improper Web Access Protection Technique Based on Proxy Cache Server," in National Conference on Computer Science and Engineering, Bangkok, Thailand, 2007.
- X. Jin, C. X. Lin, J. Luo and J. Han, "SocialSpamGuard:A Data Mining-Based Spam Detection System for Socia lMedia Networks," 2011.
- M. Bosma, E. Meij and W. Weerkamp, "A Framework For Unsupervised Spam Detection In Social Networking Sites," 2012.
- B. Markines, C. Cattuto and F. Menczer, "Social Spam Detection," in AIRWeb, 2009.
- J. Pei, B. Zhou, Z. Tang and D. Huang, Data Mining Techniques for Spam Detection.
- P. Charoenpornsawat, "Software: SWATH - Thai Word Segmentation," [Online]. Available: http://www. cs. cmu. edu/~paisarn/software. html. [Accessed 10 July 2012].
- J. W. Seifert, "Data Mining: An Overview," Congressional Research Service, 2004.
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