Towards Online Spam Filtering in Social Networks

Hongyu Gao, Yan Chen, Kathy Lee, Diana Palsetia, Alok Choudhary

Research output: Contribution to conferencePaperpeer-review

159 Scopus citations


Online social networks (OSNs) are extremely popular among Internet users. Unfortunately, in the wrong hands, they are also effective tools for executing spam campaigns. In this paper, we present an online spam filtering system that can be deployed as a component of the OSN platform to inspect messages generated by users in real-time. We propose to reconstruct spam messages into campaigns for classification rather than examine them individually. Although campaign identification has been used for offline spam analysis, we apply this technique to aid the online spam detection problem with sufficiently low overhead. Accordingly, our system adopts a set of novel features that effectively distinguish spam campaigns. It drops messages classified as "spam" before they reach the intended recipients, thus protecting them from various kinds of fraud. We evaluate the system using 187 million wall posts collected from Facebook and 17 million tweets collected from Twitter. In different parameter settings, the true positive rate reaches 80.9% while the false positive rate reaches 0.19% in the best case. In addition, it stays accurate for more than 9 months after the initial training phase. Once deployed, it can constantly secure the OSNs without the need for frequent re-training. Finally, tested on a server machine with eight cores (Xeon E5520 2.2Ghz) and 16GB memory, the system achieves an average throughput of 1580 messages/sec and an average processing latency of 21.5ms on the Facebook dataset.

Original languageEnglish (US)
StatePublished - 2012
Event19th Annual Network and Distributed System Security Symposium, NDSS 2012 - San Diego, United States
Duration: Feb 5 2012Feb 8 2012


Conference19th Annual Network and Distributed System Security Symposium, NDSS 2012
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality


Dive into the research topics of 'Towards Online Spam Filtering in Social Networks'. Together they form a unique fingerprint.

Cite this