The role of Social Bots in Deriving Misinformation on Vaccine Brands: A Text Minning Approach

This research focuses on detecting social bots on the Telegram platform using the deep learning model, Distilled-BERT. Additionally, it aims to explore the role of these bots in spreading vaccine-related (mis)information. Specifically, the study examines how social bots on Telegram influence discussions around vaccines and their manufacturers, such as Pfizer and Moderna, using key constructs of misinformation. The research employs theoretical constructs like sentiment and belief to understand the impact of social bots on vaccine-related (mis)information. To measure these constructs, a custom-built dictionary for beliefs and the TextBlob dictionary for sentiment analysis were used. By operationalizing these constructs, the study seeks to uncover the extent to which belief, rather than facts and evidence, is being used by social bots to spread vaccine information. Furthermore, the research aims to understand the level of polarity present in the discussions driven by these bots.