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Content-Aware Trust Propagation Toward Online Review Spam Detection

Authors:

Fengjun LiAuthors Info & Claims

Journal of Data and Information Quality (JDIQ), Volume 11, Issue 3

Article No.: 11, Pages 1 - 31

https://doi.org/10.1145/3305258

Published: 20 June 2019 Publication History

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Abstract

With the increasing popularity of online review systems, a large volume of user-generated content becomes available to help people make reasonable judgments about the quality of services and products from unknown providers. However, these platforms are frequently abused since fraudulent information can be freely inserted by potentially malicious users without validation. Consequently, online review systems become targets of individual and professional spammers, who insert deceptive reviews by manipulating the rating and/or the content of the reviews.

In this work, we propose a review spamming detection scheme based on the deviation between the aspect-specific opinions extracted from individual reviews and the aggregated opinions on the corresponding aspects. In particular, we model the influence on the trustworthiness of the user due to his opinion deviations from the majority in the form of a deviation-based penalty, and integrate this penalty into a three-layer trust propagation framework to iteratively compute the trust scores for users, reviews, and review targets, respectively. The trust scores are effective indicators of spammers, since they reflect the overall deviation of a user from the aggregated aspect-specific opinions across all targets and all aspects. Experiments on the dataset collected from Yelp.com show that the proposed detection scheme based on aspect-specific content-aware trust propagation is able to measure users’ trustworthiness based on opinions expressed in reviews.

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Cited By

Qazi AHasan NMao RElhag Mohamed Abo MKumar Dey SHardaker G(2024)Machine Learning-Based Opinion Spam Detection: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.339926412(143485-143499)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3399264
Zhao JShao MTang HLiu JDu LWang H(2023)RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networksKnowledge-Based Systems10.1016/j.knosys.2023.111029280(111029)Online publication date: Nov-2023
https://doi.org/10.1016/j.knosys.2023.111029
Waja GPatil GMehta CPatil S(2023)How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural NetworkInternational Journal of Information Management Data Insights10.1016/j.jjimei.2022.1001473:1(100147)Online publication date: Apr-2023
https://doi.org/10.1016/j.jjimei.2022.100147
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Index Terms

Content-Aware Trust Propagation Toward Online Review Spam Detection
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

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Published In

cover image Journal of Data and Information Quality

Journal of Data and Information Quality Volume 11, Issue 3

Special Issue on Combating Digital Misinformation and Disinformation and On the Horizon

September 2019

160 pages

ISSN:1936-1955

EISSN:1936-1963

DOI:10.1145/3331015

Editor:
Tiziana Catarci
Sapienza University of Rome, Rome, Italy

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Copyright © 2019 ACM.

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Publication History

Published: 20 June 2019

Accepted: 01 January 2019

Revised: 01 December 2018

Received: 01 May 2018

Published in JDIQ Volume 11, Issue 3

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Cited By

Qazi AHasan NMao RElhag Mohamed Abo MKumar Dey SHardaker G(2024)Machine Learning-Based Opinion Spam Detection: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.339926412(143485-143499)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3399264
Zhao JShao MTang HLiu JDu LWang H(2023)RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networksKnowledge-Based Systems10.1016/j.knosys.2023.111029280(111029)Online publication date: Nov-2023
https://doi.org/10.1016/j.knosys.2023.111029
Waja GPatil GMehta CPatil S(2023)How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural NetworkInternational Journal of Information Management Data Insights10.1016/j.jjimei.2022.1001473:1(100147)Online publication date: Apr-2023
https://doi.org/10.1016/j.jjimei.2022.100147
Sheibani SShakeri HSheibani R(2023)Four-dimensional trust propagation model for improving the accuracy of recommender systemsThe Journal of Supercomputing10.1007/s11227-023-05278-079:15(16793-16820)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s11227-023-05278-0
Sheibani SShakeri HSheibani R(2023)Applying multi-factor Beta distribution-based trust for improving accuracy of recommender systemsMultimedia Tools and Applications10.1007/s11042-023-17265-x83:14(41327-41347)Online publication date: 12-Oct-2023
https://doi.org/10.1007/s11042-023-17265-x
Cai MDu YTan YLu X(2023)Aspect-based classification method for review spam detectionMultimedia Tools and Applications10.1007/s11042-023-16293-x83:7(20931-20952)Online publication date: 5-Aug-2023
https://doi.org/10.1007/s11042-023-16293-x
Li JYang LZhang P(2023)Shooting review spam with a weakly supervised approach and a sentiment-distribution-oriented methodApplied Intelligence10.1007/s10489-022-04063-553:9(10789-10799)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s10489-022-04063-5
Wang SJiang WChen S(2023)An Aspect-Based Semi-supervised Generative Model for Online Review Spam DetectionUbiquitous Security10.1007/978-981-99-0272-9_14(207-219)Online publication date: 16-Feb-2023
https://doi.org/10.1007/978-981-99-0272-9_14
Mewada ADewang R(2022)Research on false review detection Methods: A state-of-the-art reviewJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.07.02134:9(7530-7546)Online publication date: Oct-2022
https://doi.org/10.1016/j.jksuci.2021.07.021
He LWang XChen HXu G(2022)Online Spam Review Detection: A Survey of LiteratureHuman-Centric Intelligent Systems10.1007/s44230-022-00001-32:1-2(14-30)Online publication date: 5-May-2022
https://doi.org/10.1007/s44230-022-00001-3
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