The beginnings of the COVID19 pandemic on Twitter. Computational analysis of public conversation in Spanish Language

Abstract

At the beginning of the COVID19 pandemic, social platforms played a crucial role in the production andaccess to information. This study aims to identify the topics of most significantinte rest and their associated feelings during the on set of the pandemic on Spanish-language tuits. In addition, we analyzed the role of Twitter as a social platform involved in the public conversation, both as a means for mass self-communication and for amplifying the voice of a reducedset of high visibility actors. 231,375 tweets were collectedin Spain and Latin America over two months. Then, the sample was analyzed with digital methods and techniques through computer programming in R. Frequency andsentiment indicators were measured, and terms were grouped to identify topics and determine users' interests. The frequency of the main terms is dynamic throughout the periodstudied, suggesting different perceptions of the pandemic. The main topics refer to conversations around the number of cases, deaths, and infections. Sentiment analysis shows the prevalence of negative feelings. The analyzed sample corresponds to ordinary users' messages for the great majority, but a part of it has been amplified on a large scale through retweets and bookmarks.