Abstract

The explosive use of social media also makes it a popular platform for malicious users, known as social spammers, to overwhelm normal users with unwanted content. One effective way for social spammer detection is to build a classifier based on content and social network information. However, social spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of "human" friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundation
Pages59-65
Number of pages7
Volume1
ISBN (Print)9781577356776
StatePublished - 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: Jul 27 2014Jul 31 2014

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period7/27/147/31/14

Fingerprint

Spamming
Classifiers
Processing

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Hu, X., Tang, J., & Liu, H. (2014). Online social spammer detection. In Proceedings of the National Conference on Artificial Intelligence (Vol. 1, pp. 59-65). AI Access Foundation.

Online social spammer detection. / Hu, Xia; Tang, Jiliang; Liu, Huan.

Proceedings of the National Conference on Artificial Intelligence. Vol. 1 AI Access Foundation, 2014. p. 59-65.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hu, X, Tang, J & Liu, H 2014, Online social spammer detection. in Proceedings of the National Conference on Artificial Intelligence. vol. 1, AI Access Foundation, pp. 59-65, 28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, Quebec City, Canada, 7/27/14.
Hu X, Tang J, Liu H. Online social spammer detection. In Proceedings of the National Conference on Artificial Intelligence. Vol. 1. AI Access Foundation. 2014. p. 59-65
Hu, Xia ; Tang, Jiliang ; Liu, Huan. / Online social spammer detection. Proceedings of the National Conference on Artificial Intelligence. Vol. 1 AI Access Foundation, 2014. pp. 59-65
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