Online Social Spammer Detection


  • Xia Hu Arizona State University
  • Jiliang Tang Arizona State University
  • Huan Liu Arizona State University



social spammer, online learning, spam, social media, social networks


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.




How to Cite

Hu, X., Tang, J., & Liu, H. (2014). Online Social Spammer Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).