This paper addresses the issue of deceptive opinion spam, which are fictitious reviews written to sound authentic and deceive readers. The authors develop and compare three approaches to detecting such spam: (1) standard text categorization using n-gram features, (2) psycholinguistic deception detection based on psychological effects of lying, and (3) genre identification treating deceptive and truthful reviews as sub-genres of imaginative and informative writing, respectively. They create a large-scale dataset with 800 deceptive and 800 truthful reviews for evaluation. The results show that the combined classifier using both n-gram and psychological features achieves nearly 90% accuracy, outperforming individual methods. The study also reveals insights into the nature of deceptive opinions, such as their tendency to focus on external aspects and the difficulties liars face in encoding spatial information. The findings highlight the importance of considering both contextual and motivational factors in deception detection.This paper addresses the issue of deceptive opinion spam, which are fictitious reviews written to sound authentic and deceive readers. The authors develop and compare three approaches to detecting such spam: (1) standard text categorization using n-gram features, (2) psycholinguistic deception detection based on psychological effects of lying, and (3) genre identification treating deceptive and truthful reviews as sub-genres of imaginative and informative writing, respectively. They create a large-scale dataset with 800 deceptive and 800 truthful reviews for evaluation. The results show that the combined classifier using both n-gram and psychological features achieves nearly 90% accuracy, outperforming individual methods. The study also reveals insights into the nature of deceptive opinions, such as their tendency to focus on external aspects and the difficulties liars face in encoding spatial information. The findings highlight the importance of considering both contextual and motivational factors in deception detection.