COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies

Abstract

Background: False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global public health. Misinformation originating from various sources has been spreading on the web since the beginning of the COVID-19 pandemic. Antivaccine activists have also begun to use platforms such as Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of social media, it is of great importance to gather the relevant data.

Objective: In this paper, we describe a data set of Twitter posts and Twitter accounts that publicly exhibit a strong antivaccine stance. The data set is made available to the research community via our AvaxTweets data set GitHub repository. We characterize the collected accounts in terms of prominent hashtags, shared news sources, and most likely political leaning.

Methods: We started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific antivaccine-related keywords. Then, we collected the historical tweets of the set of accounts that engaged in spreading antivaccination narratives between October 2020 and December 2020, leveraging the Academic Track Twitter API. The political leaning of the accounts was estimated by measuring the political bias of the media outlets they shared.

Results: We gathered two curated Twitter data collections and made them publicly available: (1) a streaming keyword-centered data collection with more than 1.8 million tweets, and (2) a historical account-level data collection with more than 135 million tweets. The accounts engaged in the antivaccination narratives lean to the right (conservative) direction of the political spectrum. The vaccine hesitancy is fueled by misinformation originating from websites with already questionable credibility.

Conclusions: The vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering progress toward vaccine-induced herd immunity, and could potentially increase the number of infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Because data access is the first obstacle to attain this goal, we published a data set that can be used in studying antivaccine misinformation on social media and enable a better understanding of vaccine hesitancy.

Keywords: COVID-19; COVID-19 vaccines; SARS-CoV-2; Twitter; conspiracy; dataset; hesitancy; misinformation; network analysis; public health; social media; trust; utilization; vaccine; vaccine hesitancy.

Figure 1

Number of tweets over time in the streaming collection. The times of adverse events related to vaccines are marked by dashed vertical lines. Further descriptions of the news items are provided in the legend below the chart. CDC: US Centers for Disease Control and Prevention; FDA: US Food and Drug Administration.

Figure 2

Geographical distribution of the tweets from the streaming collection originating in the United States. The location of the tweets was inferred from the self-reported location of the account. Top: absolute number of tweets in each state; bottom: number of tweets normalized by the state population.

Figure 3

Tweets in the account collection. Left: distribution of tweets per account; right: distribution of tweets over time.

Figure 4

Top 10 news sources in the streaming collection. The URLs of the news aggregators and the large social platforms were omitted.

Figure 5

Top 10 URLs in the account collection. The URLs of the news aggregators and the large social platforms were omitted.

Figure 6

Distributions of the Twitter accounts based on their political leaning and attitude toward vaccination. The political leaning of each account was calculated from its media diet. Anti-vax: antivaccination.

Figure 7

An overview of the prominent hashtags in the data set, clustered into 3 communities. The nodes are the hashtags, and the links are drawn between two hashtags that appear together in the same tweet. Clustering was performed using the Louvain algorithm. For readability, we do not show all the node labels.