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The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News

Authors:

Kyumin LeeAuthors Info & Claims

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Pages 275 - 284

https://doi.org/10.1145/3209978.3210037

Published: 27 June 2018 Publication History

Abstract

A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11%~33%. Our source code and dataset are available at http://web.cs.wpi.edu/~kmlee/data/gau.html.

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Cited By

Marcondes FBarbosa MGala AAlmeida JNovais P(2024)Emotional and Mental Nuances and Technological Approaches: Optimising Fact-Check Dissemination through Cognitive Reinforcement TechniqueElectronics10.3390/electronics1301024013:1(240)Online publication date: 4-Jan-2024
https://doi.org/10.3390/electronics13010240
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Wang SWang WZhang XWang YLiu HChen FBaeza-Yates RBonchi F(2024)A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671944(3200-3211)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671944
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Index Terms

The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social recommendation

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Published In

cover image ACM Conferences

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

June 2018

1509 pages

ISBN:9781450356572

DOI:10.1145/3209978

General Chairs:
Kevyn Collins-Thompson
University of Michigan, United States
,
Qiaozhu Mei
University of Michigan, United States
,
Program Chairs:
Brian Davison
Lehigh University, United States
,
Yiqun Liu
Tsinghua University, China
,
Emine Yilmaz
University College London, United Kingdom

Copyright © 2018 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: 27 June 2018

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SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval

July 8 - 12, 2018

MI, Ann Arbor, USA

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SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Marcondes FBarbosa MGala AAlmeida JNovais P(2024)Emotional and Mental Nuances and Technological Approaches: Optimising Fact-Check Dissemination through Cognitive Reinforcement TechniqueElectronics10.3390/electronics1301024013:1(240)Online publication date: 4-Jan-2024
https://doi.org/10.3390/electronics13010240
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Wang SWang WZhang XWang YLiu HChen FBaeza-Yates RBonchi F(2024)A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671944(3200-3211)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671944
He BMa YAhamad MKumar S(2024)Corrective or Backfire: Characterizing and Predicting User Response to Social CorrectionProceedings of the 16th ACM Web Science Conference10.1145/3614419.3644004(149-158)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3614419.3644004
Wu JXu WLiu QWu SWang L(2024)Adversarial Contrastive Learning for Evidence-Aware Fake News Detection With Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334164036:11(5591-5604)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3341640
Sharma SDatta ASharma R(2024)AMIR: An Automated Misinformation Rebuttal System—A COVID-19 Vaccination Datasets-Based ExpositionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.342952611:6(7723-7733)Online publication date: Dec-2024
https://doi.org/10.1109/TCSS.2024.3429526
Sreevarshan SVishal Vaseekaran KKishore P(2024)Application of BiMPM Technique to Detect the Fake News2024 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IoT60841.2024.10550280(1-5)Online publication date: 17-Apr-2024
https://doi.org/10.1109/IC3IoT60841.2024.10550280
Ahmed KKhan MHaq IMazroa AM.S. SInnab NAlajmi MAlkahtani H(2024)Social media’s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferationExpert Systems with Applications10.1016/j.eswa.2024.124650255(124650)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124650
Mu YNiu PBontcheva KAletras N(2024)Predicting and analyzing the popularity of false rumors in WeiboExpert Systems with Applications10.1016/j.eswa.2023.122791243(122791)Online publication date: Jun-2024
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Sallami DAïmeur E(2024)Exploring beyond detection: a review on fake news prevention and mitigation techniquesJournal of Computational Social Science10.1007/s42001-024-00351-x8:1Online publication date: 30-Dec-2024
https://doi.org/10.1007/s42001-024-00351-x
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