A study of effective features for detecting long-surviving Twitter spam accounts

International Conference on Advanced Communication Technology, ICACT

Volumn , Issue , 2013, Pages 841-846

  Lin, Po Ching ^a   Huang, Po Min ^a  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

Author keywords

detection; effective features; interaction rate; Twitter spam; URL rate

Indexed keywords

COMMON FEATURES; DETECTION ACCURACY; DETECTION FEATURES; EFFECTIVE FEATURES; INTERACTION RATE; PUBLIC ACCOUNTS; TWITTER SPAM; URL RATE;

COMMUNICATION; ERROR DETECTION; SOCIAL NETWORKING (ONLINE); WEB SERVICES;

INTERNET;

EID: 84876233788     PISSN: 17389445     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Population of social networks world wide, http://technified.netiinterneti social-networks-world-wide/555.
2. S. Abu-Nimeh, T. Chen, and O. Alzubi, "Malicious and spam posts in online social networks." IEEE Computer Society, 44(9), Sep. 2011.
3. K. Thomas, and D. M. Nicol, "The koobface botnet and the rise of social malware, " In Proceedings of the 5th International Conference on Malicious and Unwanted Software, Oct. 2010.
4. G. Yan, G. Chen, S. Eidenbenz, and N. Li, "Mal ware propagation in online social networks: nature, dynamics, and defense implication, " In Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, Mar. 2011.
5. REST API resource, https://dev.twitter.comldocs/api.
6. Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia, "Who is tweeting on Twitter: human, bot, or cyborg?" In Proceedings of the 26th Annual Computer Security Applications Conference(ACM), Dec. 2010.
7. F. Benevenuto, G. Magno, T.Rodrigues and Y. Almeida, "Detect spammers on Twitter, " In Proceedings of the 7th Annual Collaboration, Electronic messaging, AntiAbuse and Spam Conference(CEAS), July 2010.
8. C. Yang, R. C. Harkreader, and G. GU, "Die free or live hard? Empirical evaluation and new design for fighting evolving Twitter spammers, " In Proceedings of the 14th International Symposium on Recent Advances in Intrusion Detection(RAID), Sept. 2011.
9. J. Song, S. Lee, and J. Kim, "Spam Filtering in Twitter using SenderReceiver Relationship, " In Proceedings of International Symposium on Recent Advances in Intrusion Detection (RAID), Sept. 2011.
10. C. M. Zhang, and Y. Paxson, "Detecting and analyzing automated activity on Twitter, " In Proceedings of the 12th international conference on Passive and active measurement(PAM), Apr. 2011.
11. G. Stringhini, C. Kruegel, and G. Vigna, "Detecting spammers on social networks, " In Proceedings of the 26th Annual Computer Security Applications Conference(ACM), Dec. 2010.
12. K. Lee, J. Caverlee, and S. Webb, "Uncovering social spammers: social honey pots + machine learning, " In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, July 2010.
13. K. Thomas, C. Grier, Y. Paxson, and D. Song, "Suspend account in retrospect: an analysis of Twitter spam, " In Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference, Nov. 2011.
14. The definition of Spam on Twitter Help Center, https://support.twitter. comlgroups/31-twitter-basics/topics/114-guidelines-best-practices/articles/ 18311-the-twitter-rules.
15. Twitter's rate limiting, https://dev.twitter.comldocs/rate-limiting.
16. The PHP library for working with Twitter's OAuth API, https://github. com/abraham/twitteroauth.
17. Weka, the machine learning tool, http://www.cs.waikato.ac.nz/m1/weka.
18. CUSUM, http://en.wikipedia.org/wiki/CUSUM.
19. K. Thomas, C. Grier, J. Ma, Y. Paxson, and D. Song, "Design and evaluation of a real-time URL spam filtering service, " In Proceedings of the 2011 IEEE Symposium on Security and Privacy, May 2011.