{"title":"Content-driven detection of campaigns in social media","authors":"Kyumin Lee, James Caverlee, Zhiyuan Cheng, D. Sui","doi":"10.1145/2063576.2063658","DOIUrl":null,"url":null,"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 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.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"12 1","pages":"551-556"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2063576.2063658","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39
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 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.