Content-driven detection of campaigns in social media Conference Paper uri icon


  • We study the problem of detecting coordinated free text campaigns in large-scale social media. These campaigns - ranging from coordinated spam messages to promotional and advertising campaigns to political astro-turfing - are growing in significance and reach with the commensurate rise of massive-scale social systems. Often linked by common "talking points", there has been little research in detecting these campaigns. Hence, we propose and evaluate a content-driven framework for effectively linking free text posts with common "talking points" and extracting campaigns from large-scale social media. One of the salient aspects of the framework is an investigation of graph mining techniques for isolating coherent campaigns from large message-based graphs. Through an experimental study over millions of Twitter messages we identify five major types of campaigns - Spam, Promotion, Template, News, and Celebrity campaigns - and we show how these campaigns may be extracted with high precision and recall. © 2011 ACM.

author list (cited authors)

  • Lee, K., Caverlee, J., Cheng, Z., & Sui, D. Z.

citation count

  • 21

editor list (cited editors)

  • Macdonald, C., Ounis, I., & Ruthven, I.

publication date

  • January 2011