Vaccine hesitancy has been a growing public health issue, but during COVID-19, understanding vaccine hesitancy and promote vaccine favorability takes on a troubling immediacy. With the growing political polarization on scientific issues, the COVID-19 vaccine-related sentiment has recently been divided across ideological lines. This study aims to understand how vaccine favorability and specific vaccine-related concerns including possible side effects, distrust in medical professionals, and conspiratorial beliefs concerning COVID-19 vaccines were articulated and transmitted by Twitter users from opposing ideological camps and with different follower scopes. Using a combination of computational approaches, including supervised machine-learning and structural topic modeling, we examined tweets surrounding COVID-19 vaccination ( N = 16,959) from 1 March to 30 June 2020. Results from linear mixed-effects models suggested that Twitter users high on conservative ideology and with a standard instead of large follower scope tend to express less favorable vaccine-related sentiments and talk more about vaccine side effects, distrust of medical professionals, and conspiracy theories. There is also an interaction effect where liberals with large follower scope expressed the least amount of distrust of medical professionals, whereas extreme conservatives expressed greater distrust for health professionals, regardless of their follower scope. Finally, structural topic modeling revealed distinct topical focuses among liberal and conservative users. Theoretical and practical implications for leveraging social media in effective health communication practice were discussed.

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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.

We propose a three-pronged framework to study discourses surrounding social media activism initiated by networked counterpublics: personalized expressions that share stories and support, demands for changes that address systematic problems, and contentions between various actors and perspectives. Situating our analysis in discourses related to sexual violence and gender justice activism on Twitter, Facebook, Instagram, and Reddit, we use supervised machine learning to quantify three discourses—networked acknowledgment, calls to action, and feminism contention—and apply time series analysis to model their interrelations. Results show that networked acknowledgment stimulated both calls to action and feminism contention and that calls to action predicted feminism contention across all platforms. These discourses were more sensitive to real-world events on Twitter and Facebook, but more ephemeral and cyclical on Instagram and more persistent and coupled on Reddit. Our findings speak to the opportunities and challenges in social media activism and underscore cross-platform similarities and differences.

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