Spam detection on twitter using traditional classifiers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6906 LNCS, Issue , 2011, Pages 175-186

  McCord, M ^a   Chuah, M ^a  

^a Lehigh University   (United States)

Author keywords

machine learning; Social network security; spam detection

Indexed keywords

CONTENT-BASED FEATURES; DETECTION SCHEME; F-MEASURE; LEGITIMATE USERS; RANDOM FOREST CLASSIFIER; SOCIAL NETWORKING SITES; SOCIAL NETWORKS; SPAM DETECTION; SPAM MESSAGES; SPAMMERS; LARGE AMOUNTS;

DECISION TREES; DETECTORS; NETWORK SECURITY; SOCIAL NETWORKING (ONLINE); SOCIAL SCIENCES COMPUTING; FEATURE EXTRACTION; LEARNING SYSTEMS; TRUSTED COMPUTING;

INTERNET; SOCIAL NETWORKING (ONLINE);

EID: 80052767040     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23496-5_13     Document Type: Conference Paper

References (19)

1. Mowbray, M.: The Twittering Machine. In: Proceedings of the 6th International Conference on Web Information and Technologies (April 2010)
2. Analytics, P.: Twitter study (August 2009), http://www.peranalytics.com/ blog/wpcontent/ uploads/2010/05/Twitter-Study-August-2009.pdf
3. CNET. 4 chan may be behind attack on twitter (2009), http://news.cnet. com/8301-13515-3-10279618-26.html
4. How to; 5 Top methods & applications to reduce Twitter Spam, http://blog.thoughtpick.com/2009/07/how-to-5-top-methodsapplications- to-reduce-twitter-spam.html
5. Twitter, Restoring accidentally suspended accounts (2009a), http://status.twitter.com/post/136164828/restoringaccidentally- suspended-accounts
6. Twitter. The twitter rules (2009b), http://status.twitter.com/post/ 136164828/restoringaccidentally- suspended-accounts
7. Rish, I.: An empirical study of the naïve bayes classifier. In: Proeedings of IJCAI Workshop on Empirical Methods in Artificial Intelligence (2005)
8. Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289-1305 (2003)
9. Compete site comparison, http://siteanalytics.compete.com/ facebookcom+myspace.com+twi tter.com/
10. Sophos facebook id probe (2008), http://www.sophos.com/pressoffice/news/ articles/2007/08/face book.html
11. Bilge, L., et al.: All your contacts are belong to us: automated identifty theft attacks on social networks. In: Proceedings of ACM World Wide Web Conference (2009)
12. Jagatic, T.N., et al.: Social Phishing. Communications of ACM 50(10), 94-100 (2007)
13. Yardi, S., et al.: Detecting Spam in a Twitter Network. First Monday 15(1) (2010)
14. Stringhini, G., Kruegel, C., Vigna, G.: Detecting Spammers on Social Networks. In: Proceedings of ACM ACSAS 2010 (December 2010)
15. Wang, A.H.: Don't Follow me: Twitter Spam Detection. In: Proceedings of 5th International Conference on Security and Cryptography (July 2010)
16. Platt, J.: Sequential Minimal Optimization: A fast algorithm for training support vector machines. In: Schoelkopf, B., et al. (eds.) Advanced in Kernel Methods - Support Vector Learning. MIT Press, Cambridge
17. Berger, H., Kohle, M., Merkl, D.: On the impact of document representation on classifier performance in email categorization. In: Proceedings of the 4th International Conference on Information Systems Technology and IST Applications (May 2005)
18. Aha, D., Kibler, D.: Instance-based Learning Algorithms. Machine Learning 6, 37-66
19. Breiman, L.: Random Forests. Machine Learning 45(1) (October 2001)