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Merging multiple criteria to identify suspicious reviews
Author(s)
Date Issued
2010
Date Available
2010-08-18T15:18:55Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2010 by the Association for Computing Machinery, Inc.
Subject – LCSH
User-generated content--Evaluation
Recommender systems (Information filtering)--Evaluation
Machine learning
Communication--Network analysis
Web versions
Language
English
Status of Item
Peer reviewed
Journal
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
This item is made available under a Creative Commons License
File(s)
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Name
recsys-open.pdf
Size
268.69 KB
Format
Adobe PDF
Checksum (MD5)
342c9addcd713b2c32a647054c759910
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