Content-driven detection of campaigns in social media

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 contentdriven 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. Categories and Subject Descriptors: H.3.5 [Online Information Services]: Web-based services; J.4 [Computer Applications]: Social and behavioral sciences