Characterizing Twitter Interaction during COVID-19 pandemic using Complex Networks and Text Mining
This provides insights into social media dynamics during a crisis for researchers and policymakers in public health and communication, though it is incremental as it applies existing methods to new data.
The paper analyzed Twitter interactions in South American countries during the COVID-19 pandemic using complex networks and text mining, finding patterns like degree distributions and adjacency matrices that suggest systemic behaviors and potential bot activity.
The outbreak of covid-19 started many months ago, the reported origin was in Wuhan Market, China. Fastly, this virus was propagated to other countries because the access to international travels is affordable and many countries have a distance of some flight hours, besides borders were a constant flow of people. By the other hand, Internet users have the habits of sharing content using Social Networks and issues, problems, thoughts about Covdid-19 were not an exception. Therefore, it is possible to analyze Social Network interaction from one city, country to understand the impact generated by this global issue. South America is one region with developing countries with challenges to face related to Politics, Economy, Public Health and other. Therefore, the scope of this paper is to analyze the interaction on Twitter of South American countries and characterize the flow of data through the users using Complex Network representation and Text Mining. The preliminary experiments introduces the idea of existence of patterns, similar to Complex Systems. Besides, the degree distribution confirm the idea of having a System and visualization of Adjacency Matrices show the presence of users' group publishing and interacting together during the time, there is a possibility of identification of robots sending posts constantly.