# Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision

arXiv: https://arxiv.org/abs/2210.10031 · HTML: https://mlsanity.com/paper/2210.10031/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Identifying Morality Frames in Political Tweets using Relational Learning](https://mlsanity.com/paper/2109.04535/) (2021)
- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies](https://mlsanity.com/paper/2105.05134/) (2021)
- [Political Framing: US COVID19 Blame Game](https://mlsanity.com/paper/2007.09655/) (2020)
- [Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter](https://mlsanity.com/paper/2103.06960/) (2021)
- [Weakly Supervised Multi-task Learning for Concept-based Explainability](https://mlsanity.com/paper/2104.12459/) (2021)
- [Twitter User Representation Using Weakly Supervised Graph Embedding](https://mlsanity.com/paper/2108.08988/) (2021)
- [From Hesitancy Framings to Vaccine Hesitancy Profiles: A Journey of Stance, Ontological Commitments and Moral Foundations](https://mlsanity.com/paper/2202.09456/) (2022)
- [Psycho-linguistic differences among competing vaccination communities on social media](https://mlsanity.com/paper/2111.05237/) (2021)

## Built on by (most cited first, 9 of 9)

- [A Data Fusion Framework for Multi-Domain Morality Learning](https://mlsanity.com/paper/2304.02144/) (2023)
- [Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy](https://mlsanity.com/paper/2404.10259/) (2024)
- [Weakly Supervised Learning for Analyzing Political Campaigns on Facebook](https://mlsanity.com/paper/2210.10669/) (2022)
- [Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMs](https://mlsanity.com/paper/2403.10707/) (2024)
- [Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation](https://mlsanity.com/paper/2410.05401/) (2024)
- [Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging](https://mlsanity.com/paper/2305.06174/) (2023)
- [The Moralization Corpus: Frame-Based Annotation and Analysis of Moralizing Speech Acts across Diverse Text Genres](https://mlsanity.com/paper/2512.15248/) (2025)
- [Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media](https://mlsanity.com/paper/2502.01991/) (2025)
- [Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation](https://mlsanity.com/paper/2604.09626/) (2026)

## 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.