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Modern Information Retrieval
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges
IEEE Internet Computing
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Detecting spammers and content promoters in online video social networks
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Uncovering social spammers: social honeypots + machine learning
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Distance matters: geo-social metrics for online social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
Detecting and characterizing social spam campaigns
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Tips, dones and todos: uncovering user profiles in foursquare
Proceedings of the fifth ACM international conference on Web search and data mining
Understanding and combating link farming in the twitter social network
Proceedings of the 21st international conference on World Wide Web
Evolution of a location-based online social network: analysis and models
Proceedings of the 2012 ACM conference on Internet measurement conference
Detection of spam tipping behaviour on foursquare
Proceedings of the 22nd international conference on World Wide Web companion
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Location Based Social Networks (LBSNs) are attracting new users in exponential rates. LBSNs like Foursquare and Gowalla allow users to share their geographic location with friends, search for interesting places as well as posting tips about existing locations. By allowing users to comment on locations, LBSNs increasingly have to deal with a new wave of spammers, which aim at advertising unsolicited messages on tips and comments about locations. In this paper, we investigated the task of identifying tip spam on a popular Brazilian LBSN system, namely Apontador. Based on a labeled collection of tips provided by Apontador as well as crawled information about users and locations, we identified a number of attributes able to distinguish spam from non-spam tips. We leveraged our characterization study towards a spam detection mechanism. Using a classification technique, we were able to correctly identify 84% of spam tips and 91.8% of non-spam tips. Our results also highlight the importance that places and related user activity have for detecting tip spam on LBSNs.