JDS Journal of Data Science 1683-8602 1680-743X 1680-743X School of Statistics, Renmin University of China JDS1001 10.6339/21-JDS1001 Data Science in Action Community Detection in Google Searches Related to “Coronavirus” https://orcid.org/0000-0002-7825-7573 WaggonerPhilip D.pdwaggoner@uchicago.edu12∗ Computational Social Science, University of Chicago, Chicago, IL, USA Institute for Social and Economic Research and Policy, Columbia University, New York, NY, USA Email: pdwaggoner@uchicago.edu. 20212222021 192334347 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.

9112020 1012021 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

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.

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