Assessing the role of social bots during the early COVID-19 pandemic: infodemic, disagreement and criticism (Preprint)

BACKGROUND Social media has changed the way we live and communicate. These tools are an unprecedented instrument that may lead to great opportunities for improving many aspects of our lives, including health promotion and disease prevention. However, social media also presents a dark side that is not always so evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation. OBJECTIVE This work aims to study the role of social media bots during the COVID-19 outbreak. METHODS We use Twitter Stream API to collect tweets about Covid-19 during the early outbreak. Then, we use Botometer to obtain the probability that each account is a bot or not. We use bot classification along with topic modeling techniques to understand the Twitter conversation. Finally, we compare the sentiment associated with the tweets regarding the source of the tweet. RESULTS With respect to the topics of conversation, we found notable differences between the different accounts. The content associated with no-bots accounts is associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content is mostly news. That is the existence of diagnostic tests, the evolution of the pandemic, scientific findings. Finally, in the case of bots, the content is mostly political. Above all, a general tone of criticism and disagreement predominates. In relation to sentiment analysis, the biggest differences are associated with the tone of the conversation. Conversation in the case of self-declared bots tends to be neutral. While the conversation of normal users scores positively. In contrast, bots tend to score negatively. CONCLUSIONS By classifying the accounts according to the probability of being bots together with topic modeling we were able to segment the Twitter conversation about Covid. The tone of messages written by non-bots accounts tends to be more positive than the former. On the contrary, the tone of non-declared bots is always more negative than the tone of self-declared ones.