Uncovering the Online Social Structure Surrounding COVID-19

Abstract

How do people talk about COVID-19 online? To address this question, we offer an unsupervised framework that allows us to examine Twitter framings of the pandemic. Our approach employs a network-based exploration of social media data to identify, categorize, and understand communication patterns about the novel coronavirus on Twitter. The simplest structure that emerges from our analysis is the distinction between the internal/personal, external/global, and generic threat framings of the pandemic. This structure replicates in different Twitter samples and is validated using the variation of information measure, reflecting the significance and stability of our findings. Such an exploratory study is useful for understanding the contours of the natural, non-random structure in this online space. We contend that this understanding of structure is necessary to address a host of causal, supervised, and related questions downstream.

Keywords

• covid-19
• networks
• community detection
• twitter
• exploratory data analysis