Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Making large-scale support vector machine learning practical
Advances in kernel methods
Fusion Via a Linear Combination of Scores
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
MailRank: using ranking for spam detection
Proceedings of the 14th ACM international conference on Information and knowledge management
Topical TrustRank: using topicality to combat web spam
Proceedings of the 15th international conference on World Wide Web
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A reference collection for web spam
ACM SIGIR Forum
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
An Unsupervised Learning Algorithm for Rank Aggregation
ECML '07 Proceedings of the 18th European conference on Machine Learning
Detecting spam blogs: a machine learning approach
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Large-scale bot detection for search engines
Proceedings of the 19th international conference on World wide web
iRANK: A rank-learn-combine framework for unsupervised ensemble ranking
Journal of the American Society for Information Science and Technology
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding unusual review patterns using unexpected rules
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 20th international conference companion on World wide web
Finding deceptive opinion spam by any stretch of the imagination
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Review Graph Based Online Store Review Spammer Detection
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Understanding and combating link farming in the twitter social network
Proceedings of the 21st international conference on World Wide Web
Spotting fake reviewer groups in consumer reviews
Proceedings of the 21st international conference on World Wide Web
Estimating the prevalence of deception in online review communities
Proceedings of the 21st international conference on World Wide Web
Survey on web spam detection: principles and algorithms
ACM SIGKDD Explorations Newsletter
Learning to identify review spam
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Review spam detection via temporal pattern discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Syntactic stylometry for deception detection
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or to demote some target products. In recent years, fake review detection has attracted significant attention from both the business and research communities. However, due to the difficulty of human labeling needed for supervised learning and evaluation, the problem remains to be highly challenging. This work proposes a novel angle to the problem by modeling spamicity as latent. An unsupervised model, called Author Spamicity Model (ASM), is proposed. It works in the Bayesian setting, which facilitates modeling spamicity of authors as latent and allows us to exploit various observed behavioral footprints of reviewers. The intuition is that opinion spammers have different behavioral distributions than non-spammers. This creates a distributional divergence between the latent population distributions of two clusters: spammers and non-spammers. Model inference results in learning the population distributions of the two clusters. Several extensions of ASM are also considered leveraging from different priors. Experiments on a real-life Amazon review dataset demonstrate the effectiveness of the proposed models which significantly outperform the state-of-the-art competitors.