Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Details Finding Deceptive Opinion Spam by Any Stretch of the Imagination Finding Deceptive Opinion Spam by Any Stretch of the Imagination

2011-07-22

arxiv.org/abs/1107.4557v1

Proceedings of ACL 2011: HLT, pp. 309-319

Computer Science

Computation and Language

11 pages, 5 tables, data available at: http://www.cs.cornell.edu/~myleott

Scientific paper

Consumers increasingly rate, review and research products online. 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 opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

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