Merging multiple criteria to identify suspicious reviews

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Title: Merging multiple criteria to identify suspicious reviews
Authors: Wu, Guangyu
Greene, Derek
Cunningham, Pádraig
Permanent link: http://hdl.handle.net/10197/2403
Date: 2010
Abstract: Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor, given the rewards that can be derived from posting false or biased reviews. In this paper we present a number of criteria that might be indicative of suspicious reviews and evaluate alternative methods for integrating these criteria to produce a unified 'suspiciousness' ranking. The criteria derive from characteristics of the network of reviewers and also from analysis of the content and impact of reviews and ratings. The integration methods that are evaluated are singular value decomposition and the unsupervised hedge algorithm. These alternatives are evaluated in a user study on TripAdvisor reviews, where volunteers were asked to rate the suspiciousness of reviews that have been highlighted by the criteria.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2010 by the Association for Computing Machinery, Inc.
Keywords: Machine learningNetwork analysisAnomalous structure detection
Subject LCSH: User-generated content--Evaluation
Recommender systems (Information filtering)--Evaluation
Machine learning
Communication--Network analysis
Other versions: http://dx.doi.org/10.1145/1864708.1864757
Language: en
Status of Item: Peer reviewed
Is part of: RecSys '10 : Proceedings of the fourth ACM conference on Recommender systems :September 26–30, 2010 Barcelona, Spain
Conference Details: ACM Recommender Systems 2010, Barcelona, September 26-30, 2010
ISBN: 978-1-60558-906-0
Appears in Collections:Computer Science Research Collection

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