Characterizing information leaders in Twitter during COVID-19 Pandemic

TL;DR

This paper develops a framework to identify influential leaders on Twitter during COVID-19 by analyzing social graph metrics, aiming to distinguish reliable information sources from misinformation to improve pandemic response.

Contribution

It introduces a novel method combining social graph centrality metrics with user popularity to characterize and visualize influential leaders in Twitter during COVID-19.

Findings

Centrality metrics effectively identify key information leaders.

Clusters of leaders reveal influential communities.

Framework can support detection of positive influence in social networks.

Abstract

Information is key during a crisis such as the one produced by the current COVID-19 pandemic as it greatly shapes people opinion, behavior and their psychology. Infodemic of misinformation is an important secondary crisis associated to the pandemic. Infodemics can amplify the real negative consequences of the pandemic in different dimensions: social, economic and even sanitary. For instance, infodemics can lead to hatred between population groups that fragment the society influencing its response or result in negative habits that help the pandemic propagate. On the contrary, reliable and trustful information along with messages of hope and solidarity can be used to control the pandemic, build safety nets and help promote resilience. We propose the foundation of a framework to characterize leaders in Twitter based on the analysis of the social graph derived from the activity in this social network. Centrality metrics are used to characterize the topology of the network and the nodes as potential leaders. These metrics are compared with the user popularity metrics managed by Twitter. We then assess the resulting topology of clusters of leaders visually. We propose this tool to be the basis for a system to detect and empower users with a positive influence in the collective behavior of the network and the propagation of information.