Understanding Online Conversation about COVID-19 Vaccine on Twitter: Data Mining Approach (Preprint)
BACKGROUND Timely vaccination against COVID-19 can prevent a large number of people from getting infected. However, given the disease novelty and fast vaccine development, some people are hesitant to vaccinate. Online social networks like Twitter produce huge amounts of public health information and impact peoples' vaccination decisions. Hence, it is important to understand the conversation around the COVID-19 vaccination through the lens of social media. OBJECTIVE The present study aimed to define the nature of a larger Twitter conversation around the COVID-19 vaccine and explored interaction patterns between Twitter users engaged in such a conversation. METHODS Data collection took place in November 2020 on the wave of the news about the COVID-19 vaccine breakthrough. In total, 9600 Twitter posts were analyzed using a combination of text and network analysis. RESULTS Results of this study show that mixed-emotions reactions and discussions about potential side effects and vaccine safety dominated the online conversation. Twitter was primarily used for two purposes: information dissemination and opinion expression. Overall, the communication network was sparse, non-reciprocal, decentralized, and highly modular. Four main network clusters highlighted different groups of conversation stakeholders. CONCLUSIONS This study provides important insights into public sentiments, information-seeking behaviors, and online communication patterns during a major COVID-19 crisis. Given the popularity of Twitter among different types of communities and its power for rapid information dissemination, it can be an effective tool for vaccination promotion. Thus, it should be actively used to promote safe and effective vaccination through major stakeholders in the government, science, and health sectors.