Detecting product review spammers using rating behaviors

By Ee-Peng Lim, Viet Yen Nguyen, Nitin Jindal, Bing Liu, Hady W. Lauw. International Conference on Information and Knowledge Management, 2010

846 citations recorded by the source corpus.

This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their impact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a subset of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer evaluation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are effective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/750cdab6da520ecd9e27c88c13274e5a474e83d1

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Citations in the corpus

Review spam detection via temporal pattern discovery (2012, background): “In this way, the singleton review spam detection problem is mapped to a abnormally correlated pattern detection problem.”
Finding unusual review patterns using unexpected rules (2010, background): “In [4], a user study shows that rating behaviors are good indicators of spam.”
A Survey of Opinion Mining and Sentiment Analysis (2012, methodology): “Experimental results of both papers [40, 56] using amazon.”
Estimating the prevalence of deception in online review communities (2012, background): “Related work [8, 11] has hypothesized that the relative impact of spam reviews”
Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015, background): “A large body of work applied machine learning techniques to distinguish between behaviors (activities and proﬁle characteristics) of Sybil and non-Sybil identities [12,26,37,38].”
Synthetic review spamming and defense (2013, background): “In [11, 13], researchers focus on detecting disruptive review spam”
Opinion Fraud Detection in Online Reviews by Network Effects (2013, methodology): “A large body of work on fraud detection relies on review text information (Jindal and Liu 2008; Ott et al. 2011; Feng, Banerjee, and Choi 2012) or behavioral evidence (Lim et al. 2010; Xie et al. 2012; Feng et al. 2012), and ignore the connectivity structure of review data.”
Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus (2016, background): “, 2011), word duplication and number of sentences (Bhattarai, Rus and Dasgupta, 2009), and different reviewer- and behavior-related characteristics based on machine-learning methods (Jindal and Liu, 2008; Li et al., 2011; Lim et al., 2010).”
Detecting opinion spams and fake news using text classification (2018, background): “3 Reviewer behavior-based detection models Lim et al.(23) proposed a model based on the assumption that a spammer will target certain products or a brand to make the most impact.”