Negative deceptive opinion spam

The rising influence of user-generated online reviews (Cone, 2011) has led to growing in-centive for businesses to solicit and manufac-ture DECEPTIVE OPINION SPAM—fictitious reviews that have been deliberately written to sound authentic and deceive the reader. Re-cently, Ott et al. (2011) have introduced an opinion spam dataset containing gold standard deceptive positive hotel reviews. However, the complementary problem of negative deceptive opinion spam, intended to slander competitive offerings, remains largely unstudied. Follow-ing an approach similar to Ott et al. (2011), in this work we create and study the first dataset of deceptive opinion spam with negative sen-timent reviews. Based on this dataset, we find that standard n-gram text categorization tech-niques can detect negative deceptive opinion spam with performance far surpassing that of human judges. Finally, in conjunction with the aforementioned positive review dataset, we consider the possible interactions between sentiment and deception, and present initial results that encourage further exploration of this relationship.