Syntactic clustering of the Web
Selected papers from the sixth international conference on World Wide Web
Collection statistics for fast duplicate document detection
ACM Transactions on Information Systems (TOIS)
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
Attacking Recommender Systems: A Cost-Benefit Analysis
IEEE Intelligent Systems
CSV: visualizing and mining cohesive subgraphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SpotSigs: robust and efficient near duplicate detection in large web collections
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A Distributed Algorithm to Enumerate All Maximal Cliques in MapReduce
FCST '09 Proceedings of the 2009 Fourth International Conference on Frontier of Computer Science and Technology
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Detecting and characterizing social spam campaigns
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Detecting collective attention spam
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
An analysis of socware cascades in online social networks
Proceedings of the 22nd international conference on World Wide Web
Campaign extraction from social media
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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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.