ABSTRACT
The World Health Organization defines vaccine hesitancy as a delay in acceptance or refusal of vaccination despite the availability of vaccination services. Vaccine hesitancy contributes to lower rates of vaccination in a population and delayed vaccine coverage. A large number of COVID-19 vaccines have been administered worldwide against COVID-19. Due to concerns people have about COVID-19 vaccine adverse events, a significant proportion of people exhibit hesitancy towards the vaccines. These are often prompted by information and misinformation spread through social media conversation, which is not driven exclusively by genuine human-run accounts. Social bots have been shown to be very active during the pandemic participating in discussions about vaccines, including the spread of conflicting and misleading information. Using a novel ensemble technique, we sought to identify and describe the involvement of social bots in COVID-19 vaccination-related discussions on Twitter and how this could have influenced sentiments and hesitancies about COVID-19 vaccines. We included tweets from January to December 2021 to present a whole year's analysis in relation to the vaccines. Unique usernames from these posts were passed to Botometer and Tweetbotornot, programs that review Twitter accounts, to detect a broad range of social bots using a scoring system. A domain-oriented transfer learning technique is applied by finetuning the CT-BERT V2 model to detect the influence of social bots on COVID-19 vaccine sentiments. We computed the ratio of sentiment transmission from bots-to-human, human-to-human, human-to-bots, and bots-to-bots. BERTopic was used to extract the topics of discussion to identify the amplified or transferred hesitancies. Social bots' participation in online discussions noticeably influenced human sentiments and hesitancies about COVID-19 vaccination. A major portion of sentiments transferred from bot to human during the period of study appeared to amplify or transfer hesitancies regarding COVID-19 vaccination. © 2023 ACM.