Jul 22, 2011 · We develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate.

Hancock. 2011. Finding Deceptive Opinion Spam by Any Stretch of the Imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational ...

While recent work has focused primarily on manually identifi- able instances of opinion spam, in this work we study deceptive opinion spam—fictitious opinions ...

We develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold- ...

We develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate.

This work develops and compares three approaches to detecting deceptive opinion spam, and develops a classifier that is nearly 90% accurate on the authors' ...

Why bother? – Validates deceptive opinions. – Baseline to compare other approaches. Finding Deceptive Opinion Spam by Any Stretch of ...

Deceptive opinion spam: fictitious opinions that have been deliberately written to sound authentic. • Challenges: • Deceptive opinion spam are insidious and ...

Finding Deceptive Opinion Spam by Any Stretch of the Imagination Myle Ott Yejin Choi Claire Cardie Department of Computer Science Cornell University Ithaca ...

Finding Deceptive Opinion Spam by Any Stretch of the Imagination! • 80 truthful and 80 deceptive reviews! • 3 undergraduate judges! – Truth bias!