Towards a General Rule for Identifying Deceptive Opinion Spam

Consumers ’ purchase decisions are in-creasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the poten-tial for posting deceptive opinion spam— fictitious reviews that have been deliber-ately written to sound authentic, to de-ceive the reader. In this paper, we ex-plore generalized approaches for identify-ing online deceptive opinion spam based on a new gold standard dataset, which is comprised of data from three different do-mains (i.e. Hotel, Restaurant, Doctor), each of which contains three types of re-views, i.e. customer generated truthful re-views, Turker generated deceptive reviews and employee (domain-expert) generated deceptive reviews. Our approach tries to capture the general difference of language usage between deceptive and truthful re-views, which we hope will help customers when making purchase decisions and re-view portal operators, such as TripAdvisor or Yelp, investigate possible fraudulent ac-tivity on their sites.1