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- W2013619287 abstract "As the rapid development of China's e-commerce in recent years and the underlying evolution of adversarial spamming tactics, more sophisticated spamming activities may carry out in Chinese review websites. Empirical analysis, on recently crawled product reviews from a popular Chinese e-commerce website, reveals the failure of many state-of-the-art spam indicators on detecting collusive spammers. Two novel methods are then proposed: 1) a KNN-based method that considers the pairwise similarity of two reviewers based on their group-level relational information and selects k most similar reviewers for voting; 2) a more general graph-based classification method that jointly classifies a set of reviewers based on their pairwise transaction correlations. Experimental results show that both our methods promisingly outperform the indicator-only classifiers in various settings." @default.
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- W2013619287 date "2013-01-01" @default.
- W2013619287 modified "2023-10-10" @default.
- W2013619287 title "Uncovering collusive spammers in Chinese review websites" @default.
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- W2013619287 doi "https://doi.org/10.1145/2505515.2505700" @default.
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