COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies

Muric, Goran; Wu, Yusong; Ferrara, Emilio

BACKGROUND: False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, thus posing a threat to global public health. Misinformation originating from various sources has been spreading online since the beginning of the COVID-19 pandemic. Anti-vaccine activists have also begun to utilize platforms like Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of online social media, it is of greatest importance to gather the relevant data. OBJECTIVE: In this paper, we describe a dataset of Twitter posts and Twitter accounts that publicly exhibit a strong anti-vaccine stance. The dataset is made available to the research community via our AvaxTweets dataset 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 anti-vaccine related keywords. Then, we collect the historical tweets of the set of accounts that engaged in spreading anti-vaccination narratives between October 2020 and December 2020 leveraging the Academic Track Twitter API. The accounts' political leaning is estimated by measuring the political bias of the media outlets they share. RESULTS: We gather two curated Twitter data collections and make them publicly available: a) a streaming keyword-centered data collection with more than 1.8 million tweets, and b) a historical account-level collection with more than 135 million tweets. The accounts engaged in the anti-vaccination narratives lean right on a 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 the progress toward vaccine-induced herd immunity, and potentially increase infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Since data access is the first obstacle to attain that, we publish the dataset that can be used in studying anti-vaccine misinformation on social media and enable a better understanding of vaccine hesitancy.