mlsanityData to 7 Oct 2026

Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues

arXiv 2211.05289 ↗First posted 10 Nov 2022cs.SI cs.AI cs.CYcited by 6

Not on the map. Why?

Abstract (arXiv)

Social media has been one of the main information consumption sources for the public, allowing people to seek and spread information more quickly and easily. However, the rise of various social media platforms also enables the proliferation of online misinformation. In particular, misinformation in the health domain has significant impacts on our society such as the COVID-19 infodemic. Therefore, health misinformation in social media has become an emerging research direction that attracts increasing attention from researchers of different disciplines. Compared to misinformation in other domains, the key differences of health misinformation include the potential of causing actual harm to humans' bodies and even lives, the hardness to identify for normal people, and the deep connection with medical science. In addition, health misinformation on social media has distinct characteristics from conventional channels such as television on multiple dimensions including the generation, dissemination, and consumption paradigms. Because of the uniqueness and importance of combating health misinformation in social media, we conduct this survey to further facilitate interdisciplinary research on this problem. In this survey, we present a comprehensive review of existing research about online health misinformation in different disciplines. Furthermore, we also systematically organize the related literature from three perspectives: characterization, detection, and intervention. Lastly, we conduct a deep discussion on the pressing open issues of combating health misinformation in social media and provide future directions for multidisciplinary researchers.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 64 of its 242 references that are in the map. First posted on arXiv 10 Nov 2022.

Builds on

64 of 242 references.

References · 64

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  7. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
  8. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
  9. 2020Fact or Fiction: Verifying Scientific Claimscited by 546
  10. 2021A Survey on Automated Fact-Checkingcited by 376
  11. 2018Graph Convolutional Networks for Text Classificationcited by 345
  12. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
38 more
  1. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 209
  2. 2014A Convolutional Neural Network for Modelling Sentencescited by 163
  3. 2020To be Robust or to be Fair: Towards Fairness in Adversarial Trainingcited by 137
  4. 2021COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemiccited by 100
  5. 2022SciFact-Open: Towards open-domain scientific claim verificationcited by 94
  6. 2021Trustworthy AI: A Computational Perspectivecited by 93
  7. 2020CoAID: COVID-19 Healthcare Misinformation Datasetcited by 83
  8. 2021User Preference-aware Fake News Detectioncited by 76
  9. 2022MDFEND: Multi-domain Fake News Detectioncited by 68
  10. 2020The COVID-19 Social Media Infodemiccited by 64
  11. 2021Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Datacited by 50
  12. 2020Scientific Claim Verification with VERT5ERINIcited by 47
  13. 2020Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Armscited by 41
  14. 2022Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learningcited by 41
  15. 2019Noise-tolerant fair classificationcited by 40
  16. 2022Memory-Guided Multi-View Multi-Domain Fake News Detectioncited by 31
  17. 2020A Paragraph-level Multi-task Learning Model for Scientific Fact-Verificationcited by 29
  18. 2020Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repositorycited by 25
  19. 2021Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verificationcited by 20
  20. 2022Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Mediacited by 20
  21. 2022Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfercited by 19
  22. 2021Combat COVID-19 Infodemic Using Explainable Natural Language Processing Modelscited by 17
  23. 2022Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19cited by 15
  24. 2021Transformer based Automatic COVID-19 Fake News Detection Systemcited by 14
  25. 2021Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensemblescited by 14
  26. 2021DEAP-FAKED: Knowledge Graph based Approach for Fake News Detectioncited by 13
  27. 2022RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Mediacited by 13
  28. 2019Better Word Embeddings by Disentangling Contextual n-Gram Informationcited by 12
  29. 2020Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-casecited by 12
  30. 2021MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classificationcited by 12
  31. 2021Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in Englishcited by 11
  32. 2020Detecting Medical Misinformation on Social Media Using Multimodal Deep Learningcited by 9
  33. 2022SciTweets -- A Dataset and Annotation Framework for Detecting Scientific Online Discoursecited by 9
  34. 2017Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twittercited by 8
  35. 2021Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Informationcited by 8
  36. 2022MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detectioncited by 7
  37. 2022Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweetscited by 7
  38. 2019Detecting Fake News with Weak Social Supervisioncited by 6

50 most cited of 64 All 64 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 3.

6 papers.