Vaccine discourse during the onset of the COVID-19 pandemic: Topical structure and source patterns informing efforts to combat vaccine hesitancy

Abstract

Background: Understanding public discourse about a COVID-19 vaccine in the early phase of the COVID-19 pandemic may provide key insights concerning vaccine hesitancy. However, few studies have investigated the communicative patterns in which Twitter users participate discursively in vaccine discussions.

Objectives: This study aims to investigate 1) the major topics that emerged from public conversation on Twitter concerning vaccines for COVID-19, 2) the topics that were emphasized in tweets with either positive or negative sentiment toward a COVID-19 vaccine, and 3) the type of online accounts in which tweets with either positive or negative sentiment were more likely to circulate.

Methods: We randomly extracted a total of 349,979 COVID-19 vaccine-related tweets from the initial period of the pandemic. Out of 64,216 unique tweets, a total of 23,133 (36.03%) tweets were classified as positive and 14,051 (21.88%) as negative toward a COVID-19 vaccine. We conducted Structural Topic Modeling and Network Analysis to reveal the distinct topical structure and connection patterns that characterize positive and negative discourse toward a COVID-19 vaccine.

Results: Our STM analysis revealed the most prominent topic emerged on Twitter of a COVID-19 vaccine was "other infectious diseases", followed by "vaccine safety concerns", and "conspiracy theory." While the positive discourse demonstrated a broad range of topics such as "vaccine development", "vaccine effectiveness", and "safety test", negative discourse was more narrowly focused on topics such as "conspiracy theory" and "safety concerns." Beyond topical differences, positive discourse was more likely to interact with verified sources such as scientists/medical sources and the media/journalists, whereas negative discourse tended to interact with politicians and online influencers.

Conclusions: Positive and negative discourse was not only structured around distinct topics but also circulated within different networks. Public health communicators need to address specific topics of public concern in varying information hubs based on audience segmentation, potentially increasing COVID-19 vaccine uptake.

Fig 1. Twitter discourse volume over time.

Fig 2. The average gamma value for each topic (γ).

The document-topic probability, or the gamma value, is the estimated proportion of words from a given document that are generated from that topic.

Fig 3. A semantic network with nodes as topics and edges as their associations.

Fig 4. Overtime trend in daily relative volume by topics.

Topic1 = monetary motivation, Topic 2 = infectious disease, Topic 3 = safety concern, Topic 4 = vaccine information, Topic 5 = new normal, Topic 6 = conspiracy, Topic 7 = consolidation, topic 8 = vaccine development, Topic 9 = inherent uncertainty, Topic 10 = vaccine effectiveness, Topic 11 = clinical trial, Topic 12 = coping strategies, Topic 13 = vaccine production.

Fig 5. Difference in topical prevalence.

Moving to the left means positive- and moving to the right means negative vaccine Twitter discourse.

Fig 6. The mention network.

Fig 7. Proportion of the top mention by account type and discourse category.