Detecting product review spammers using rating behaviors

Lim, Ee-Peng; Nguyen, Viet-An; Jindal, Nihar; Liu, Bing; Lauw, Hady W.

doi:10.1145/1871437.1871557

Cited by 552 publications

(374 citation statements)

References 14 publications

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“…opinion spam) is a relatively recent research trend, so the literature is still relatively limited, but some studies show promising results. Studies include Benevenuto et al (2009) [1], Lim et al (2010) [15], Gilbert & Karrie (2010) [7], Wu et al [25,24], Ott et al (2011) [19], and Duan & Liu (2012) [5].…”

Section: Threat Analysis and Proposed Solutionsmentioning

confidence: 99%

Robustness of Trust and Reputation Systems: Does It Matter?

Jøsang

2012

IFIP Advances in Information and Communication Technology

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Abstract. Trust and reputation systems provide a foundation for security, stability, and efficiency in the online environment because of their ability to stimulate quality and to sanction poor quality. Trust and reputation scores are assumed to represent and predict future quality and behaviour and thereby to provide valuable decision support for relying parties. This assumption depends on two factors, primarily that trust and reputation scores faithfully reflect past observed quality, and secondly that future quality will be truly similar to that represented by the scores. Unfortunately, poor robustness of trust and reputation systems often makes it relatively easy to manipulate these factors, so that the fundamental assumption behind trust and reputation systems becomes questionable. On this background we discuss to what degree robustness against strategic manipulation is important for the usefulness of trust and reputation systems in general.

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Section: Threat Analysis and Proposed Solutionsmentioning

confidence: 99%

Robustness of Trust and Reputation Systems: Does It Matter?

Jøsang

2012

IFIP Advances in Information and Communication Technology

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“…They aggregated evaluation values for a reviewer's reviews in a category. Lim et al [13] proposed methods for determining whether or not a reviewer is a spammer. They used reviews that reviewers have given and their review history.…”

Section: Related Workmentioning

confidence: 99%

Estimating Reviewer Credibility Using Review Contents and Review Histories

Tanaka

Nakamura

Hijikata

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYIn recent years, user-supplied reviews have increased to become widely prevalent on many websites. Some reviewers (users who comment on items) provide valuable information. Others provide information many people already know. Our goal is to identify credible reviewers who provide valuable information. Two methods can be used to measure reviewer credibility: assessing reviewers based on the content of reviews that they have written in the past and assessing reviewers based on their review histories. By comparing these methods, we aim at obtaining knowledge to determine which method is most useful for identifying credible reviewers. Additionally, many features have been proposed for assessing reviews or reviewers in the previous methods, but they have not been compared. We compare these attributes and clarify what kinds of attribute are useful for identifying credible reviewers. key words: information credibility, product review, credible reviewer estimation, feature investigation, methodology investigation

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“…The research on the robustness of reputation systems [5][6][7][8][9][10][11][12][13] can be categorized in terms of different criteria. Regarding the choice of the dataset, [5][6][7][8] study Amazon 8 product reviews; [9][10][11][12][13] use TripAdvisor as a case study.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the choice of the dataset, [5][6][7][8] study Amazon 8 product reviews; [9][10][11][12][13] use TripAdvisor as a case study. There is a large difference between the two datasets.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the quality of a product usually does not 3 www.tripadvisor.com 4 www.yelp.com 5 http://www.dailymail.co.uk/travel/article-2013391/Tripadvisor-Hotel-ownersbribe-guests-return-good-reviews.html 6 http://www.rhinocarhire.com/Car-Hire-Blog/May-2012/Hotel-Reviews-FaultyTowers-or-The-Ritz.aspx 7 http://www.sfgate.com/technology/article/Yelp-s-trust-at-risk-from-phonyreviews-3708962.php 8 http://www.amazon.com/ change, whereas the quality of hotel service might vary over time. Regarding types of features for machine learning, textual features are used in [5,7,[9][10][11][12]; non-textual features in [5,6,8,9,12,13]. Considering detection as a supervised learning approach, [9] labels reviews by the proportion of positive feedback given to a review.…”

Section: Introductionmentioning

confidence: 99%

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Can We Identify Manipulative Behavior and the Corresponding Suspects on Review Websites Using Supervised Learning?

Duan

Zirn

2012

Secure IT Systems

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Abstract. Identification of manipulative behavior and the corresponding suspects is an essential task for maintaining robustness of reputation systems integrated by review websites. However, this task constitutes a great challenge. In this paper, we present an approach based on supervised learning to automatically detect suspicious behavior on travel websites. We distinguish between two types of manipulation, treating them as separate tasks: promoting manipulation, which is performed in order to push the reputation of a hotel, and demoting manipulation, which is used to demote competitors. Both tasks consist of three separate levels: detecting suspicious reviews (review level), suspicious reviewers (reviewer level) and suspicious objects of the reviews, i.e. hotels (object level). A separate classifier for each of the levels is trained on various sets of textual and non-textual features. We apply state-of-the-art machine learning algorithms like Support Vector Machines. The performance of our approach is evaluated on a new dataset that we created based on reviews taken from the platform TripAdvisor and which was carefully annotated by human judges. The results show that it is possible to identify manipulating reviewers and objects of manipulation with over 90% accuracy. Identifying suspicious reviews, however, seems to be a much harder task, for which our classifier achieves an accuracy of 68% detecting promoting manipulation and 84% detecting demoting manipulation. We argue that there is the need to identify more efficient features for the classification on review level. Finally, we analyze and discuss statistical characteristics of manipulative behavior based on the predictions of the reviewer and object level classifiers.

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Detecting product review spammers using rating behaviors

Cited by 552 publications

References 14 publications

Robustness of Trust and Reputation Systems: Does It Matter?

Robustness of Trust and Reputation Systems: Does It Matter?

Estimating Reviewer Credibility Using Review Contents and Review Histories

Can We Identify Manipulative Behavior and the Corresponding Suspects on Review Websites Using Supervised Learning?

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