Content-Driven Detection of Campaigns in Social Media

摘要

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.