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We have introduced three automated approaches to deceptive opinion spam detection, based on insights coming from research in computational linguistics and psychology

Finding deceptive opinion spam by any stretch of the imagination

meeting of the association for computational linguistics, (2011): 309-319

引用：1427|浏览312

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dblp.uni-trier.de dl.acm.org academic.microsoft.com arxiv.org

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opinion spamfeature analysisgold-standard opinion spam datasetdeceptive opinion spamconsumer review更多(6+)

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Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious...更多

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Indeed, a two-tailed binomial test fails to reject the null hypothesis that JUDGE 2 and JUDGE 3 perform at-chance (p = 0.003, 0.10, 0.48 for the three judges, respectively)
The psycholinguistic approach (LIWCSVM) proposed in Section 4.2 performs 3.8% more accurately (one-tailed sign test p = 0.02), and the standard text categorization approach proposed in Section 4.3 performs between 14.6% and 16.6% more accurately

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