WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Detecting spammers and content promoters in online video social networks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
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
Detecting tip spam in location-based social networks
Proceedings of the 28th Annual ACM Symposium on Applied Computing
On the validity of geosocial mobility traces
Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks
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In Foursquare, one of the currently most popular online location based social networking sites (LBSNs), users may not only check-in at specific venues but also post comments (or tips), sharing their opinions and previous experiences at the corresponding physical places. Foursquare tips, which are visible to everyone, provide venue owners with valuable user feedback besides helping other users to make an opinion about the specific venue. However, they have been the target of spamming activity by users who exploit this feature to spread tips with unrelated content. In this paper, we present what, to our knowledge, is the first effort to identify and analyze different patterns of tip spamming activity in Foursquare, with the goal of developing automatic tools to detect users who post spam tips - tip spammers. A manual investigation of a real dataset collected from Foursquare led us to identify four categories of spamming behavior, viz. Advertising/Spam, Self-promotion, Abusive and Malicious. We then applied machine learning techniques, jointly with a selected set of user, social and tip's content features associated with each user, to develop automatic detection tools. Our experimental results indicate that we are able to not only correctly distinguish legitimate users from tip spammers with high accuracy (89.76%) but also correctly identify a large fraction (at least 78.88%) of spammers in each identified category.