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TrueView: Harnessing the Power of Multiple Review Sites

Published: 18 May 2015 Publication History
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  • Abstract

    Online reviews on products and services can be very useful for customers, but they need to be protected from manipulation. So far, most studies have focused on analyzing online reviews from a single hosting site. How could one leverage information from multiple review hosting sites? This is the key question in our work. In response, we develop a systematic methodology to merge, compare, and evaluate reviews from multiple hosting sites. We focus on hotel reviews and use more than 15 million reviews from more than 3.5 million users spanning three prominent travel sites. Our work consists of three thrusts: (a) we develop novel features capable of identifying cross-site discrepancies effectively, (b) we conduct arguably the first extensive study of cross-site variations using real data, and develop a hotel identity-matching method with 93% accuracy, (c) we introduce the TrueView score, as a proof of concept that cross-site analysis can better inform the end user. Our results show that: (1) we detect 7 times more suspicious hotels by using multiple sites compared to using the three sites in isolation, and (2) we find that 20% of all hotels appearing in all three sites seem to have low trustworthiness score. Our work is an early effort that explores the advantages and the challenges in using multiple reviewing sites towards more informed decision making.

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    [3]
    Supporting webpage containing data, slides and code. www.cs.unm.edu/~aminnich/trueview.
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    Trustscore: The measurement of online reputation management - trustyou. http://www.trustyou.com/.
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    Cited By

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    • (2023)Group-based Fraud Detection Network on e-Commerce PlatformsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599836(5463-5475)Online publication date: 6-Aug-2023
    • (2022)ScoreGAN: A Fraud Review Detector Based on Regulated GAN With Data AugmentationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.313977117(280-291)Online publication date: 2022
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    Published In

    cover image ACM Other conferences
    WWW '15: Proceedings of the 24th International Conference on World Wide Web
    May 2015
    1460 pages
    ISBN:9781450334693

    Sponsors

    • IW3C2: International World Wide Web Conference Committee

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    International World Wide Web Conferences Steering Committee

    Republic and Canton of Geneva, Switzerland

    Publication History

    Published: 18 May 2015

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    Author Tags

    1. anomaly detection
    2. hotels
    3. multi-site features
    4. opinion spam
    5. review mining

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    • Research-article

    Funding Sources

    • National Science Foundation Graduate Research Fellowship
    • National Institute of Biomedical Imaging and Bioengineering
    • NSF SaTC

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    WWW '15
    Sponsor:
    • IW3C2

    Acceptance Rates

    WWW '15 Paper Acceptance Rate 131 of 929 submissions, 14%;
    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    Cited By

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    • (2024)User tendency-based rating scaling in online trading networksPLOS ONE10.1371/journal.pone.029790319:4(e0297903)Online publication date: 16-Apr-2024
    • (2023)Group-based Fraud Detection Network on e-Commerce PlatformsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599836(5463-5475)Online publication date: 6-Aug-2023
    • (2022)ScoreGAN: A Fraud Review Detector Based on Regulated GAN With Data AugmentationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.313977117(280-291)Online publication date: 2022
    • (2022)Opinion Spamming: Fake Consumer Review DetectionComputer Vision and Robotics10.1007/978-981-16-8225-4_24(307-317)Online publication date: 15-Mar-2022
    • (2020)Personalized Review Recommendation based on Users’ Aspect SentimentACM Transactions on Internet Technology10.1145/341484120:4(1-26)Online publication date: 6-Oct-2020
    • (2020)STARSACM Transactions on Intelligent Systems and Technology10.1145/339746311:5(1-25)Online publication date: 24-Jul-2020
    • (2020)GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster DetectionProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401165(689-698)Online publication date: 25-Jul-2020
    • (2020)Understanding Network Characteristics of Spam Users in Social Media2020 Eighth International Conference on Advanced Cloud and Big Data (CBD)10.1109/CBD51900.2020.00039(171-176)Online publication date: Dec-2020
    • (2020)GCNEXT: graph convolutional network with expanded balance theory for fraudulent user detectionSocial Network Analysis and Mining10.1007/s13278-020-00697-w10:1Online publication date: 8-Oct-2020
    • (2019)Influencing Opinions through False Online Information : A StudyInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT1952101(443-449)Online publication date: 5-Mar-2019
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