mlsanityData to 7 Oct 2026

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

arXiv 1806.07516 ↗First posted 20 Jun 2018cs.IR cs.SIcited by 24

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Abstract (arXiv)

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 https://github.com/nguyenvo09/CombatingFakeNews.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 5; 2023: 5; 2024: 3; 2025: 1; 2026: 1.

24 papers.

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  1. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
  2. 2021A Survey on Multimodal Disinformation Detectioncited by 47
  3. 2023Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformationcited by 39
  4. 2023Multilingual Previously Fact-Checked Claim Retrievalcited by 32
  5. 2022Evidence-aware Fake News Detection with Graph Neural Networkscited by 30
  6. 2021Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Documentcited by 19
  7. 2021Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detectioncited by 17
  8. 2021The Role of Context in Detecting Previously Fact-Checked Claimscited by 17
  9. 2023Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidencecited by 17
  10. 2022Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?cited by 16
  11. 2021Dataset of Fake News Detection and Fact Verification: A Surveycited by 11
  12. 2022"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaderscited by 9
12 more
  1. 2022Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networkscited by 7
  2. 2021A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatarcited by 5
  3. 2022Identifying and Characterizing Active Citizens who Refute Misinformation in Social Mediacited by 5
  4. 2024Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learningcited by 5
  5. 2021Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directionscited by 4
  6. 2024Search, Examine and Early-Termination: Fake News Detection with Annotation-Free Evidencescited by 3
  7. 2023Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccinationcited by 2
  8. 2021Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?cited by 0
  9. 2023AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation Systemcited by 0
  10. 2024Health Misinformation in Social Networks: A Survey of IT Approachescited by 0
  11. 2025PUBLICSPEAK: Hearing the Public with a Probabilistic Framework in Local Governmentcited by 0
  12. 2026Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?cited by 0