research-article

Deep Semantic Frame-Based Deceptive Opinion Spam Analysis

Authors:

Hyeokyoon Chang,

Jaewoo KangAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1131 - 1140

https://doi.org/10.1145/2806416.2806551

Published: 17 October 2015 Publication History

Abstract

User-generated content is becoming increasingly valuable to both individuals and businesses due to its usefulness and influence in e-commerce markets. As consumers rely more on such information, posting deceptive opinions, which can be deliberately used for potential profit, is becoming more of an issue. Existing work on opinion spam detection focuses mainly on linguistic features such as n-grams, syntactic patterns, or LIWC. However, deep semantic analysis remains largely unstudied. In this paper, we propose a frame-based deep semantic analysis method for understanding rich characteristics of deceptive and truthful opinions written by various types of individuals including crowdsourcing workers, employees who have expert-level domain knowledge about local businesses, and online users who post on Yelp and TripAdvisor. Using our proposed semantic frame feature, we developed a classification model that outperforms the baseline model and achieves an accuracy of nearly 91%. Also, we performed qualitative analysis of deceptive and truthful review datasets and considered their semantic differences. Finally, we successfully found some interesting features that existing methods were unable to identify.

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Cited By

Liu Y(2025)Signed Latent Factors for Spamming Activity DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351657320(651-664)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3516573
Chai YLiu YLi WZhu BLiu HJiang Y(2024)An interpretable wide and deep model for online disinformation detectionExpert Systems with Applications10.1016/j.eswa.2023.121588237(121588)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121588
Zaki NKrishnan ATuraev SRustamov ZRustamov JAlmusalami AAyyad FRegasa TIriho B(2024)Node embedding approach for accurate detection of fake reviews: a graph-based machine learning approach with explainable AIInternational Journal of Data Science and Analytics10.1007/s41060-024-00565-218:3(295-315)Online publication date: 4-Jun-2024
https://doi.org/10.1007/s41060-024-00565-2
Show More Cited By

Index Terms

Deep Semantic Frame-Based Deceptive Opinion Spam Analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 October 2015

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National Research Foundation of Korea

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Citations

Cited By

Liu Y(2025)Signed Latent Factors for Spamming Activity DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351657320(651-664)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3516573
Chai YLiu YLi WZhu BLiu HJiang Y(2024)An interpretable wide and deep model for online disinformation detectionExpert Systems with Applications10.1016/j.eswa.2023.121588237(121588)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121588
Zaki NKrishnan ATuraev SRustamov ZRustamov JAlmusalami AAyyad FRegasa TIriho B(2024)Node embedding approach for accurate detection of fake reviews: a graph-based machine learning approach with explainable AIInternational Journal of Data Science and Analytics10.1007/s41060-024-00565-218:3(295-315)Online publication date: 4-Jun-2024
https://doi.org/10.1007/s41060-024-00565-2
Alsubari SDeshmukh SAldhyani TAl Nefaie AAlrasheedi M(2023)Rule-Based Classifiers for Identifying Fake Reviews in E-commerce: A Deep Learning SystemFuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling10.1007/978-981-19-8566-9_14(257-276)Online publication date: 26-Mar-2023
https://doi.org/10.1007/978-981-19-8566-9_14
Maurya SSingh DMaurya A(2022)Deceptive opinion spam detection approaches: a literature surveyApplied Intelligence10.1007/s10489-022-03427-153:2(2189-2234)Online publication date: 5-May-2022
https://dl.acm.org/doi/10.1007/s10489-022-03427-1
Bian PLiu LSweetser P(2021)Detecting Spam Game Reviews on Steam with a Semi-Supervised ApproachProceedings of the 16th International Conference on the Foundations of Digital Games10.1145/3472538.3472547(1-10)Online publication date: 3-Aug-2021
https://dl.acm.org/doi/10.1145/3472538.3472547
Mohawesh RXu STran SOllington RSpringer MJararweh YMaqsood S(2021)Fake Reviews Detection: A SurveyIEEE Access10.1109/ACCESS.2021.30755739(65771-65802)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3075573
Liu Yd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Recommending Inferior Results: A General and Feature-Free Model for Spam DetectionProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411900(955-974)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3411900
Wen JHu JShi HWang XYuan CHan JGuo T(2020)Fusion-based Spammer Detection Method by Embedding Review Texts and Weak Social Relations2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00067(329-336)Online publication date: Dec-2020
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00067
Schulder MWiegand MRuppenhofer J(2020)Automatic generation of lexica for sentiment polarity shiftersNatural Language Engineering10.1017/S135132492000039X27:2(153-179)Online publication date: 9-Jul-2020
https://doi.org/10.1017/S135132492000039X
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