Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Consumers increasingly go online to rate, review and research products (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing these 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 important theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Myle Ott, Yejin Choi, Claire Cardie, Jeffrey T. Hancock
• Keywords: CORR 2011, Deceptive Opinion Spam, Deceptive Opinions, Education, Opinion Spam