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research-article

Towards a highly effective and robust Web credibility evaluation system

Authors:

Radoslaw Nielek,

Paulina Adamska,

Adam Wierzbicki, and

Karl AbererAuthors Info & Claims

Decision Support Systems, Volume 79, Issue C

Pages 99 - 108

https://doi.org/10.1016/j.dss.2015.07.010

Published: 01 November 2015 Publication History

Abstract

By leveraging crowdsourcing, Web credibility evaluation systems (WCESs) have become a promising tool to assess the credibility of Web content, e.g., Web pages. However, existing systems adopt a passive way to collect users' credibility ratings, which incurs two crucial challenges: (1) a considerable fraction of Web content have few or even no ratings, so the coverage (or effectiveness) of the system is low; (2) malicious users may submit fake ratings to damage the reliability of the system. In order to realize a highly effective and robust WCES, we propose to integrate recommendation functionality into the system. On the one hand, by fusing Matrix Factorization and Latent Dirichlet Allocation, a personalized Web content recommendation model is proposed to attract users to rate more Web pages, i.e., the coverage is increased. On the other hand, by analyzing a user's reaction to the recommended Web content, we detect imitating attackers, which have recently been recognized as a particular threat to WCES to make the system more robust. Moreover, an adaptive reputation system is designed to motivate users to more actively interact with the integrated recommendation functionality. We conduct experiments using both real datasets and synthetic data to demonstrate how our proposed recommendation components significantly improve the effectiveness and robustness of existing WCES. We study methods to improve efficiency of crowdsourcing Web credibility evaluation systems (WCESs).Recommendation algorithm (RA) increases coverage in WCES.Joint application of RA and reputation system helps fight down imitation attacks.

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Cited By

Faraon MJaff ANepomuceno LVillavicencio V(2020)Fake News and Aggregated CredibilityInternational Journal of Ambient Computing and Intelligence10.4018/IJACI.20201001.oa111:4(93-117)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.4018/IJACI.20201001.oa1
Zhou XZafarani R(2020)A Survey of Fake NewsACM Computing Surveys10.1145/339504653:5(1-40)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3395046
Schneider PWojcik GKawiak AKwasniewicz LWierzbicki A(2020)True Or False: How Does Our Brain Decide About Truth?2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB48159.2020.9277701(1-9)Online publication date: 27-Oct-2020
https://dl.acm.org/doi/10.1109/CIBCB48159.2020.9277701
Show More Cited By

Index Terms

Towards a highly effective and robust Web credibility evaluation system
1. Information systems
  1. Information retrieval

Index terms have been assigned to the content through auto-classification.

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Published In

Decision Support Systems Volume 79, Issue C

November 2015

209 pages

ISSN:0167-9236

Issue’s Table of Contents

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2015

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Cited By

Faraon MJaff ANepomuceno LVillavicencio V(2020)Fake News and Aggregated CredibilityInternational Journal of Ambient Computing and Intelligence10.4018/IJACI.20201001.oa111:4(93-117)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.4018/IJACI.20201001.oa1
Zhou XZafarani R(2020)A Survey of Fake NewsACM Computing Surveys10.1145/339504653:5(1-40)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3395046
Schneider PWojcik GKawiak AKwasniewicz LWierzbicki A(2020)True Or False: How Does Our Brain Decide About Truth?2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB48159.2020.9277701(1-9)Online publication date: 27-Oct-2020
https://dl.acm.org/doi/10.1109/CIBCB48159.2020.9277701
Kawiak AWójcik GKwasniewicz LSchneider PWierzbicki A(2020)Look Who’s Talking: Modeling Decision Making Based on Source CredibilityComputational Science – ICCS 202010.1007/978-3-030-50371-0_24(327-341)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.1007/978-3-030-50371-0_24
Kwaśniewicz ŁWójcik GKawiak ASchneider PWierzbicki A(2020)How You Say or What You Say? Neural Activity in Message Credibility EvaluationComputational Science – ICCS 202010.1007/978-3-030-50371-0_23(312-326)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.1007/978-3-030-50371-0_23
Panagopoulos AKoutrouli ETsalgatidou A(2017)Modeling and Evaluating a Robust Feedback-Based Reputation System for E-Commerce PlatformsACM Transactions on the Web10.1145/305726511:3(1-55)Online publication date: 12-Jul-2017
https://dl.acm.org/doi/10.1145/3057265
Quijano-Sanchez LLiberatore F(2017)The BIG CHASEDecision Support Systems10.1016/j.dss.2017.04.00798:C(49-58)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.dss.2017.04.007
Rafalak MDeja DWierzbicki ANielek RKąkol M(2016)Web Content Classification Using Distributions of Subjective Quality EvaluationsACM Transactions on the Web10.1145/299413210:4(1-30)Online publication date: 15-Nov-2016
https://dl.acm.org/doi/10.1145/2994132
Haralabopoulos GAnagnostopoulos IZeadally S(2016)The Challenge of Improving Credibility of User-Generated Content in Online Social NetworksJournal of Data and Information Quality10.1145/28990037:3(1-4)Online publication date: 17-Aug-2016
https://dl.acm.org/doi/10.1145/2899003

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