Campaign Extraction from Large-Scale Social Media

Abstract

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 in massive-scale social systems. Specifically, 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. Three of the salient features of the campaign extraction framework are: (i) first, we investigate graph mining techniques for isolating coherent campaigns from large message-based graphs; (ii) second, we conduct a comprehensive comparative study of text-based message correlation in message and user levels; and (iii) finally, we analyze temporal behaviors of various campaign types. 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.

Biography