mlsanityData to 14 Oct 2026

Health Misinformation in Social Networks: A Survey of IT Approaches

arXiv 2410.18670 ↗First posted 24 Oct 2024cs.SI cs.AI cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Specifically, we first present manual and automatic approaches for fact-checking. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation. We also provide a detailed list of several datasets on health misinformation and of publicly available tools. We conclude the survey with a discussion on the open challenges and future research directions in the battle against health misinformation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 51 of its 216 references that are in the map. First posted on arXiv 24 Oct 2024.

Builds on

51 of 216 references.

References · 51

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  5. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  6. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  7. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  8. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  9. 2020Fact or Fiction: Verifying Scientific Claimscited by 554
  10. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  11. 2016FastText.zip: Compressing text classification modelscited by 335
  12. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
38 more
  1. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 211
  2. 2020CORD-19: The COVID-19 Open Research Datasetcited by 180
  3. 2017A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkcited by 143
  4. 2019Fine-grained Fact Verification with Kernel Graph Attention Networkcited by 105
  5. 2021COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemiccited by 100
  6. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  7. 2022SciFact-Open: Towards open-domain scientific claim verificationcited by 94
  8. 2020CoAID: COVID-19 Healthcare Misinformation Datasetcited by 84
  9. 2020Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Societycited by 72
  10. 2018Predicting Factuality of Reporting and Bias of News Media Sourcescited by 71
  11. 2019Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approachescited by 65
  12. 2020FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19cited by 61
  13. 2022Generating Scientific Claims for Zero-Shot Scientific Fact Checkingcited by 59
  14. 2020MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformationcited by 37
  15. 2020Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Datasetcited by 32
  16. 2018The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake Newscited by 24
  17. 2021Claim Detection in Biomedical Twitter Postscited by 18
  18. 2021Transformer based Automatic COVID-19 Fake News Detection Systemcited by 14
  19. 2022RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Mediacited by 13
  20. 2021MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classificationcited by 12
  21. 2019BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Taskscited by 11
  22. 2021Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in Englishcited by 11
  23. 2020Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learnedcited by 10
  24. 2021Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Resultscited by 10
  25. 2021TROLLMAGNIFIER: Detecting State-Sponsored Troll Accounts on Redditcited by 10
  26. 2021Project Debater APIs: Decomposing the AI Grand Challengecited by 8
  27. 2022VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccinescited by 7
  28. 2020What Types of COVID-19 Conspiracies are Populated by Twitter Bots?cited by 6
  29. 2022Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issuescited by 6
  30. 2017Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accountscited by 5
  31. 2020Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19cited by 5
  32. 2022CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Researchcited by 4
  33. 2018Fake Cures: User-centric Modeling of Health Misinformation in Social Mediacited by 3
  34. 2017Strategies and Influence of Social Bots in a 2017 German state election - A case study on Twittercited by 2
  35. 2022A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Modelscited by 2
  36. 2017People on Drugs: Credibility of User Statements in Health Communitiescited by 1
  37. 2020Detecting Troll Behavior via Inverse Reinforcement Learning: A Case Study of Russian Trolls in the 2016 US Electioncited by 1
  38. 2021Sentiment Analysis for Troll Detection on Weibocited by 1

50 most cited of 51 All 51 (JSON)

Built on by

Not cited yet.