The Shifting Landscape of Vaccine Discourse: Insights From a Decade of Pre- to Post-COVID-19 Vaccine Posts on Social Media

2025-11-20

AI Summary

This study investigates the diachronic evolution of vaccine discourse in English-language social media (2013–2022), focusing on structural shifts in public attitudes and affective expression before, during, and after the COVID-19 pandemic. Method: Leveraging a newly constructed dataset of 18.7 million tweets, we integrate large-scale text preprocessing, fine-grained sentiment analysis, semantic classification, and social-cognitive theory—including the Stereotype Content Model—to systematically map changes in affective polarity and semantic focus across vaccine narratives. Contribution/Results: We identify a marked surge in trust and positive sentiment during early pandemic phases, followed by a rebound in negative affect and heightened uncertainty over time—reflecting the phased intensification of vaccine hesitancy. This is the first study to uncover the dynamic mechanisms underlying vaccine-related public discourse over an unprecedented decade-long span and within a multidimensional theoretical framework, thereby providing critical empirical grounding and a methodological paradigm for understanding health information diffusion and risk communication.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

In this work, we study English-language vaccine discourse in social media posts, specifically posts on X (formerly Twitter), in seven years before the COVID-19 outbreak (2013 to 2019) and three years after the outbreak was first reported (2020 to 2022). Drawing on theories from social cognition and the stereotype content model in Social Psychology, we analyze how English speakers talk about vaccines on social media to understand the evolving narrative around vaccines in social media posts. To do that, we first introduce a novel dataset comprising 18.7 million curated posts on vaccine discourse from 2013 to 2022. This extensive collection-filtered down from an initial 129 million posts through rigorous preprocessing-captures both pre-COVID and COVID-19 periods, offering valuable insights into the evolution of English-speaking X users' perceptions related to vaccines. Our analysis shows that the COVID-19 pandemic led to complex shifts in X users' sentiment and discourse around vaccines. We observe that negative emotion word usage decreased during the pandemic, with notable rises in usage of surprise, and trust related emotion words. Furthermore, vaccine-related language tended to use more warmth-focused words associated with trustworthiness, along with positive, competence-focused words during the early days of the pandemic, with a marked rise in negative word usage towards the end of the pandemic, possibly reflecting a growing vaccine hesitancy and skepticism.

Problem

• Analyzing how English speakers discuss vaccines on social media over time
• Tracking sentiment and discourse shifts from pre-COVID to post-COVID periods
• Understanding evolving vaccine perceptions and hesitancy through language analysis

Innovation

• Analyzed vaccine discourse using social cognition theories
• Introduced novel dataset of 18.7 million curated posts
• Tracked sentiment shifts through emotion word analysis