Searching for Spam: Detecting Fraudulent Accounts via Web Search
Twitter users are harassed increasingly often by unsolicited messages that waste time and mislead users into clicking nefarious links. While increasingly powerful methods have been designed to detect spam, many depend on complex methods that require training and analyzing message content. While many of these systems are fast, implementing them in real time could present numerous challenges.
Previous work has shown that large portions of spam originate from fraudulent accounts. We therefore propose a system which uses web searches to determine if a given account is fraudulent. The system uses the web searches to measure the online presence of a user and labels accounts with insufficient web presence to likely be fraudulent. Using our system on a collection of actual Twitter messages, we are able to achieve a true positive rate over 74% and a false positive rate below 11%, a detection rate comparable to those achieved by more expensive methods.
Given its ability to operate before an account has produced a single tweet, we propose that our system could be used most effectively by combining it with slower more expensive machine learning methods as a first line of defense, alerting the system of fraudulent accounts before they have an opportunity to inject any spam into the ecosystem.
- 2.Gao, H., Chen, Y., Lee, K., Palsetia, D., Choudhary, A.: Towards Online Spam Filtering in Social Networks. In: Proceedings of the 19th Annual Network & Distributed System Security Symposium (February 2012)Google Scholar
- 4.Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.: Detecting and characterizing social spam campaigns. In: Proceedings of the 10th Annual Conference on Internet Measurement, IMC 2010, pp. 35–47. ACM, New York (2010)Google Scholar
- 5.Thomas, K., Grier, C., Ma, J., Vern, P., Song, D.: Design and evaluation of a real-time url spam filtering service. In: 2011 IEEE Symposium on Security and Privacy, SP, pp. 447–462 (May 2011)Google Scholar
- 6.Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting Spammers on Twitter. In: Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, CEAS (July 2010)Google Scholar
- 7.Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots + machine learning. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 435–442. ACM, New York (2010)Google Scholar
- 9.Wang, A.: Don’t follow me: Spam detection in twitter. In: Proceedings of the 2010 International Conference on Security and Cryptography, SECRYPT, pp. 1–10 (July 2010)Google Scholar
- 11.Yardi, C., Romero, D., Schoenebeck, G., Boyd, D.: Detecting spam in a twitter network. First Monday 15(1) (2010)Google Scholar
- 13.Yerva, S., Miklós, Z., Aberer, K.: What have fruits to do with technology?: the case of orange, blackberry and apple. In: Proceedings of the International Conference on Web Intelligence, Mining and Semantics, WIMS 2011, pp. 48:1–48:10. ACM, New York (2011)Google Scholar