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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.


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Uncovering the Online Social Structure Surrounding COVID-19

Show Author's information Philip D. Waggoner1( )Robert Y. Shapiro2Samuel Frederick2Ming Gong3
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

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
Received: 20 February 2021 Revised: 02 June 2021 Accepted: 26 June 2021 Published: 23 August 2021 Issue date: June 2021
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Publication history
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Publication history

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

Rights and permissions

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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