mlsanityData to 7 Oct 2026

Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision

arXiv 2210.10031 ↗First posted 18 Oct 2022cs.CL cs.CY cs.LGcited by 9

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Abstract (arXiv)

In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the COVID vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 69 references that are in the map. First posted on arXiv 18 Oct 2022.

Builds on

14 of 69 references.

References · 14

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  6. 2021Identifying Morality Frames in Political Tweets using Relational Learningcited by 26
  7. 2022A Holistic Framework for Analyzing the COVID-19 Vaccine Debatecited by 18
  8. 2021COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiraciescited by 17
  9. 2020Political Framing: US COVID19 Blame Gamecited by 8
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2 more
  1. 2022From Hesitancy Framings to Vaccine Hesitancy Profiles: A Journey of Stance, Ontological Commitments and Moral Foundationscited by 4
  2. 2021Psycho-linguistic differences among competing vaccination communities on social mediacited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 2; 2026: 1.

9 papers.

  1. 2023A Data Fusion Framework for Multi-Domain Morality Learningcited by 14
  2. 2024Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategycited by 9
  3. 2022Weakly Supervised Learning for Analyzing Political Campaigns on Facebookcited by 8
  4. 2024Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMscited by 8
  5. 2024Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluationcited by 4
  6. 2023Analysis of Climate Campaigns on Social Media using Bayesian Model Averagingcited by 3
  7. 2025The Moralization Corpus: Frame-Based Annotation and Analysis of Moralizing Speech Acts across Diverse Text Genrescited by 2
  8. 2025Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Mediacited by 1
  9. 2026Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotationcited by 0