Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Collision Module Integration in a Specific Graphic Engine for Terrain Visualization
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving web spam classifiers using link structure
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Know your neighbors: web spam detection using the web topology
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Filtering spam with behavioral blacklisting
Proceedings of the 14th ACM conference on Computer and communications security
Spamming botnets: signatures and characteristics
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Application of social relation graphs for early detection of transient spammers
WSEAS Transactions on Information Science and Applications
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining (Social) Network Graphs to Detect Random Link Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
BotGraph: large scale spamming botnet detection
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
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
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Uncovering social spammers: social honeypots + machine learning
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Spam detection with a content-based random-walk algorithm
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Detecting and characterizing social spam campaigns
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Detecting spammers on social networks
Proceedings of the 26th Annual Computer Security Applications Conference
Spam or ham?: characterizing and detecting fraudulent "not spam" reports in web mail systems
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
An Unsupervised Approach for Identifying Spammers in Social Networks
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Using social network analysis for spam detection
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
OCTracker: A Density-Based Framework for Tracking the Evolution of Overlapping Communities in OSNs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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The popularity of Online Social Networks (OSNs) is often faced with challenges of dealing with undesirable users and their malicious activities in the social networks. The most common form of malicious activity over OSNs is spamming wherein a bot (fake user) disseminates content, malware/viruses, etc. to the legitimate users of the social networks. The common motives behind such activity include phishing, scams, viral marketing and so on which the recipients do not indent to receive. It is thus a highly desirable task to devise techniques and methods for identifying spammers (spamming accounts) in OSNs. With an aim of exploiting social network characteristics of community formation by legitimate users, this paper presents a community-based framework to identify spammers in OSNs. The framework uses community-based features of OSN users to learn classification models for identification of spamming accounts. The preliminary experiments on a real-world dataset with simulated spammers reveal that proposed approach is promising and that using community-based node features of OSN users can improve the performance of classifying spammers and legitimate users.