Network and behavioural patterns of bots during the early COVID-19 infodemic

Abstract

Objective: Show bot behaviors on Twitter during the COVID-19 infodemic. In this paper, we examined the roles of bots in the case of the COVID-19 infodemic and the diffusion of non-credible information such as ``5G'' and ``Bill Gates'' conspiracy theories and ``Trump'' and ``WHO'' related contents by analyzing retweet networks and retweeted items. We collected more than 2 million COVID-19 related tweets and used Botometer to identify bots and human users in the retweet network filled with conspiracy theories emerged in the COVID-19 infodemic. We then labelled users as many as possible by examining published credible and non-credible URLs. In addition, we examined the bots and human behavior by counting the frequency of retweeting and finally revealed the top popular words list of human and bots by calculating TF-IDF. We show the segregated topology of their retweet networks, which indicates that right-wing self-media accounts and conspiracy theorists may lead to this opinion cleavage, while malicious bots might favor amplification of the diffusion of non-credible information. Although the basic influence of information diffusion could be larger in human users than bots, the effects of bots are non-negligible under an infodemic situation.<gdiv></gdiv>