Community Detection in Google Searches Related to “Coronavirus”

Waggoner, Philip D.

doi:10.6339/21-JDS1001

Journal of Data Science

Community Detection in Google Searches Related to “Coronavirus”

Volume 19, Issue 2 (2021): Special issue: Continued Data Science Contributions to COVID-19 Pandemic, pp. 334–347

Philip D. Waggoner

https://doi.org/10.6339/21-JDS1001

Pub. online: 22 February 2021 Type: Data Science In Action

Received
9 November 2020

Accepted
10 January 2021

Published
22 February 2021

Abstract

Coronavirus and the COVID-19 pandemic have substantially altered the ways in which people learn, interact, and discover information. In the absence of everyday in-person interaction, how do people self-educate while living in isolation during such times? More specifically, do communities emerge in Google search trends related to coronavirus? Using a suite of network and community detection algorithms, we scrape and mine all Google search trends in America related to an initial search for “coronavirus,” starting with the first Google search on the term (January 16, 2020) to recently (August 11, 2020). Results indicate a near-constant shift in the structure of how people educate themselves on coronavirus. Queries in the earliest days focusing on “Wuhan” and “China”, then shift to “stimulus checks” at the height of the virus in the U.S., and finally shift to queries related to local surges of new cases in later days. A few communities emerge surrounding terms more overtly related to coronavirus (e.g., “cases”, “symptoms”, etc.). Yet, given the shift in related Google queries and the broader information environment, clear community structure for the full search space does not emerge.

Supplementary material

Supplementary Material

The following are included in online Supplementary Material: – Network Node Degrees (Full Network). – Network of Google Search Trends: January. – Network of Google Search Trends: February. – Network of Google Search Trends: March. – Network of Google Search Trends: April. – Network of Google Search Trends: May. – Network of Google Search Trends: June. – Network of Google Search Trends: July. – Network of Google Search Trends: August. – Replication R code.

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computational social science data science network science text mining

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