Towards a General Rule for Identifying Deceptive Opinion Spam
نویسندگان
چکیده
Consumers’ purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting deceptive opinion spam— fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. In this paper, we explore generalized approaches for identifying online deceptive opinion spam based on a new gold standard dataset, which is comprised of data from three different domains (i.e. Hotel, Restaurant, Doctor), each of which contains three types of reviews, i.e. customer generated truthful reviews, 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 reviews, which we hope will help customers when making purchase decisions and review portal operators, such as TripAdvisor or Yelp, investigate possible fraudulent activity on their sites.1
منابع مشابه
Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN
As a mainly network of Internet naval activities, the deceptive opinion spam is of great harm. The identification of deceptive opinion spam is of great importance because of the rapid and dramatic development of Internet. The effective distinguish between positive and deceptive opinion plays an important role in maintaining and improving the Internet environment. Deceptive opinion spam is very ...
متن کاملAutomatic detection of deceptive opinions using automatically identified specific details
Distinguishing deceptive opinions — that is, fabricated views disguised to be genuine — from honest opinions is a hard problem. Deceptive opinions can include things like the false expression of a controversial opinion, a misleading review of an item or service bought online, or deceitful interviews. Unlike many tasks involving language, detecting deceptive opinions through text alone turns out...
متن کاملFinding Deceptive Opinion Spam by Any Stretch of the Imagination
Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). 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 auth...
متن کاملNegative Deceptive Opinion Spam
The rising influence of user-generated online reviews (Cone, 2011) has led to growing incentive for businesses to solicit and manufacture DECEPTIVE OPINION SPAM—fictitious reviews that have been deliberately written to sound authentic and deceive the reader. Recently, Ott et al. (2011) have introduced an opinion spam dataset containing gold standard deceptive positive hotel reviews. However, th...
متن کاملLinguistic Models of Deceptive Opinion Spam
of the talk Consumers increasingly inform their purchase decisions with opinions and other information found on the Web. Unfortunately, the ease of posting content online, potentially anonymously, combined with the public's trust and growing reliance on this content, creates opportunities and incentives for abuse. This is especially worrisome in the case of online reviews of products and servic...
متن کامل