  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.
