WebBase: a repository of Web pages
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Socialtrust: tamper-resilient trust establishment in online communities
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
LEET'08 Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats
Predicting web spam with HTTP session information
Proceedings of the 17th ACM conference on Information and knowledge management
Social Networks as an Attack Platform: Facebook Case Study
ICN '09 Proceedings of the 2009 Eighth International Conference on Networks
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Proceedings of the 4th Workshop on Social Network Systems
Design and Evaluation of a Real-Time URL Spam Filtering Service
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Efficient and scalable socware detection in online social networks
Security'12 Proceedings of the 21st USENIX conference on Security symposium
Analysis and identification of spamming behaviors in Sina Weibo microblog
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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Social networks such as Facebook, MySpace, and Twitter have become increasingly important for reaching millions of users. Consequently, spammers are increasing using such networks for propagating spam. Existing filtering techniques such as collaborative filters and behavioral analysis filters are able to significantly reduce spam, each social network needs to build its own independent spam filter and support a spam team to keep spam prevention techniques current. We propose a framework for spam detection which can be used across all social network sites. There are numerous benefits of the framework including: 1) new spam detected on one social network, can quickly be identified across social networks; 2) accuracy of spam detection will improve with a large amount of data from across social networks; 3) other techniques (such as blacklists and message shingling) can be integrated and centralized; 4) new social networks can plug into the system easily, preventing spam at an early stage. We provide an experimental study of real datasets from social networks to demonstrate the flexibility and feasibility of our framework.