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Open Access

Uncovering the Online Social Structure Surrounding COVID-19

Institute for Social and Economic Research and Policy, Columbia University, New York City, NY 10027, USA
Department of Political Science, Columbia University, New York City, NY 10027, USA
Department of Quantitative Methods in the Social Sciences, Columbia University, New York City, NY 10027, USA
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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-19community detectiontwitternetworksexploratory data analysis

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Journal of Social Computing
Volume 2 Issue 2,
June 2021
Pages 157-165
DOI: 10.23919/JSC.2021.0008
Cite this article:
Waggoner PD, Shapiro RY, Frederick S, et al. Uncovering the Online Social Structure Surrounding COVID-19. Journal of Social Computing, 2021, 2(2): 157-165. https://doi.org/10.23919/JSC.2021.0008

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Received: 20 February 2021
Revised: 02 June 2021
Accepted: 26 June 2021
Published: 23 August 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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