Detecting spam in a Twitter network


  • Sarita Yardi Georgia Tech
  • Daniel Romero Cornell University
  • Grant Schoenebeck UC Berkeley
  • danah boyd Microsoft Research New England



social network analysis, microblogging, spam


Spam becomes a problem as soon as an online communication medium becomes popular. Twitter’s behavioral and structural properties make it a fertile breeding ground for spammers to proliferate. In this article we examine spam around a one-time Twitter meme—“robotpickuplines”. We show the existence of structural network differences between spam accounts and legitimate users. We conclude by highlighting challenges in disambiguating spammers from legitimate users.

Author Biographies

Sarita Yardi, Georgia Tech

PhD candidate in Social Computing at Georgia Tech

Daniel Romero, Cornell University

PhD student at the Center for Applied Mathematics of Cornell University.

Grant Schoenebeck, UC Berkeley

PhD candidate in theoretical computer science at University of California, Berkeley

danah boyd, Microsoft Research New England

Social Media Researcher at Microsoft Research New England and a Fellow at Harvard University's Berkman Center for Internet and Society.




How to Cite

Yardi, S., Romero, D., Schoenebeck, G., & boyd, danah. (2009). Detecting spam in a Twitter network. First Monday, 15(1).