Abstract
Social bots have already infiltrated social media platforms, such as Twitter, Facebook, and so on. Exploring the role of social bots in discussions of the COVID-19 pandemic, as well as comparing the behavioral differences between social bots and humans, is an important foundation for studying public health opinion dissemination. We collected data on Twitter and used Botometer to classify users into social bots and humans. Machine learning methods were used to analyze the characteristics of topic semantics, sentiment attributes, dissemination intentions, and interaction patterns of humans and social bots. The results show that 22% of these accounts were social bots, while 78% were humans, and there are significant differences in the behavioral characteristics between them. Social bots are more concerned with the topics of public health news than humans are with individual health and daily lives. More than 85% of bots' tweets are liked, and they have a large number of followers and friends, which means they have influence on internet users' perceptions about disease transmission and public health. In addition, social bots, located mainly in Europe and America countries, create an "authoritative" image by posting a lot of news, which in turn gains more attention and has a significant effect on humans. The findings contribute to understanding the behavioral patterns of new technologies such as social bots and their role in the dissemination of public health information.