Deceptive review detection using labeled and unlabeled data

• Published in: Multimedia tools and applications 2017-02, Vol.76 (3), p.3187-3211
• Main Authors: Rout, Jitendra Kumar, Singh, Smriti, Jena, Sanjay Kumar, Bakshi, Sambit
• Format: Article
• Language: English
• Subjects: Automation; Computer Communication Networks; Computer Science; Consumers; Data mining; Data Structures and Information Theory; Datasets; Decisions; Electronic commerce; False information; Information management; Internet; Multimedia; Multimedia Information Systems; Product reviews; Searching; Sentiment analysis; Social networks; Social research; Spamming; Special Purpose and Application-Based Systems; Studies; User generated content; Web site reviews
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-016-3819-y

Availability of millions of products and services on e-commerce sites makes it difficult to search the best suitable product according to the requirements because of existence of many alternatives. To get rid of this the most popular and useful approach is to follow reviews of others in opinionated social medias, who have already tried them. Almost all e-commerce sites provide facility to the users for giving views and experience of the product and services they experienced. The customers reviews are increasingly used by individuals, manufacturers and retailers for purchase and business decisions. As there is no scrutiny over the reviews received, anybody can write anything unanimously which conclusively leads to review spam. Moreover, driven by the desire of profit and/or publicity, spammers produce synthesized reviews to promote some products/brand and demote competitors products/brand. Deceptive review spam has seen a considerable growth overtime. In this work, we have applied supervised as well as unsupervised techniques to identify review spam. Most effective feature sets have been assembled for model building. Sentiment analysis has also been incorporated in the detection process. In order to get best performance some well-known classifiers were applied on labeled dataset. Further, for the unlabeled data, clustering is used after desired attributes were computed for spam detection. Additionally, there is a high chance that spam reviewers may also be held responsible for content pollution in multimedia social networks, because nowadays many users are giving the reviews using their social network logins. Finally, the work can be extended to find suspicious accounts responsible for posting fake multimedia contents into respective social networks.