mlsanityData to 7 Oct 2026

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

arXiv 2205.01817 ↗First posted 3 May 2022cs.CL cs.CYcited by 18

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Abstract (arXiv)

The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the low-supervision settings.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

8 of 38 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Armscited by 41
  3. 2020An Exploratory Study of COVID-19 Misinformation on Twittercited by 33
  4. 2021Identifying Morality Frames in Political Tweets using Relational Learningcited by 26
  5. 2021COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiraciescited by 17
  6. 2020Modeling Content and Context with Deep Relational Learningcited by 16
  7. 2021Model Generalization on COVID-19 Fake News Detectioncited by 11
  8. 2020Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executivescited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 7; 2025: 2; 2026: 1.

18 papers.

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  1. 2023Interactive Concept Learning for Uncovering Latent Themes in Large Text Collectionscited by 15
  2. 2023A Data Fusion Framework for Multi-Domain Morality Learningcited by 14
  3. 2024The Nature of NLP: Analyzing Contributions in NLP Paperscited by 12
  4. 2022Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervisioncited by 9
  5. 2024Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategycited by 9
  6. 2024Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMscited by 8
  7. 2023Towards Few-Shot Identification of Morality Frames using In-Context Learningcited by 6
  8. 2023An Interactive Framework for Profiling News Media Sourcescited by 4
  9. 2023"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Predictioncited by 4
  10. 2023Analysis of Climate Campaigns on Social Media using Bayesian Model Averagingcited by 3
  11. 2023Interactively Learning Social Media Representations Improves News Source Factuality Detectioncited by 3
  12. 2024A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challengescited by 3
6 more
  1. 2024Framing in the Presence of Supporting Data: A Case Study in U.S. Economic Newscited by 2
  2. 2025Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Mediacited by 1
  3. 2025Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertisingcited by 1
  4. 2024#EpiTwitter: Public Health Messaging During the COVID-19 Pandemiccited by 0
  5. 2024Effects of Collaboration on the Performance of Interactive Theme Discovery Systemscited by 0
  6. 2026Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotationcited by 0