mlsanityData to 7 Oct 2026

From Hesitancy Framings to Vaccine Hesitancy Profiles: A Journey of Stance, Ontological Commitments and Moral Foundations

arXiv 2202.09456 ↗First posted 18 Feb 2022cs.CL cs.SIcited by 4

Not on the map. Why?

Abstract (arXiv)

While billions of COVID-19 vaccines have been administered, too many people remain hesitant. Twitter, with its substantial reach and daily exposure, is an excellent resource for examining how people frame their vaccine hesitancy and to uncover vaccine hesitancy profiles. In this paper we expose our processing journey from identifying Vaccine Hesitancy Framings in a collection of 9,133,471 original tweets discussing the COVID-19 vaccines, establishing their ontological commitments, annotating the Moral Foundations they imply to the automatic recognition of the stance of the tweet authors toward any of the CoVaxFrames that we have identified. When we found that 805,336 Twitter users had a stance towards some CoVaxFrames in either the 9,133,471 original tweets or their 17,346,664 retweets, we were able to derive nine different Vaccine Hesitancy Profiles of these users and to interpret these profiles based on the ontological commitments of the frames they evoked in their tweets and on value of their stance towards the evoked frames.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 18 Feb 2022.

Builds on

11 of 34 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2017Graph Attention Networkscited by 8,854
  4. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  5. 2019Passage Re-ranking with BERTcited by 629
  6. 2020Hopfield Networks is All You Needcited by 461
  7. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  8. 2018Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategiescited by 47
  9. 2020Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Mediacited by 27
  10. 2020Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19cited by 3
  11. 2021The University of Texas at Dallas HLTRI's Participation in EPIC-QA: Searching for Entailed Questions Revealing Novel Answer Nuggetscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.

4 papers.