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. 2020 Nov:111:103601.
doi: 10.1016/j.jbi.2020.103601. Epub 2020 Oct 14.

Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis

Jiye Kwon  1 , Connor Grady  2 , Josemari T Feliciano  3 , Samah J Fodeh  4
Affiliations

Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis

Jiye Kwon et al. J Biomed Inform. 2020 Nov.

Abstract

Objectives: Using Twitter, we aim to (1) define and quantify the prevalence and evolution of facets of social distancing during the COVID-19 pandemic in the US in a spatiotemporal context and (2) examine amplified tweets among social distancing facets.

Materials and methods: We analyzed English and US-based tweets containing "coronavirus" between January 23-March 24, 2020 using the Twitter API. Tweets containing keywords were grouped into six social distancing facets: implementation, purpose, social disruption, adaptation, positive emotions, and negative emotions.

Results: A total of 259,529 unique tweets were included in the analyses. Social distancing tweets became more prevalent from late January to March but were not geographically uniform. Early facets of social distancing appeared in Los Angeles, San Francisco, and Seattle: the first cities impacted by the COVID-19 outbreak. Tweets related to the "implementation" and "negative emotions" facets largely dominated in combination with topics of "social disruption" and "adaptation", albeit to lesser degree. Social disruptiveness tweets were most retweeted, and implementation tweets were most favorited.

Discussion: Social distancing can be defined by facets that respond to and represent certain events in a pandemic, including travel restrictions and rising case counts. For example, Miami had a low volume of social distancing tweets but grew in March corresponding with the rise of COVID-19 cases.

Conclusion: The evolution of social distancing facets on Twitter reflects actual events and may signal potential disease hotspots. Our facets can also be used to understand public discourse on social distancing which may inform future public health measures.

Keywords: COVID-19; Infectious disease outbreak; Infodemiology; Social distancing; Social media.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Trends in daily tweet proportions of social distancing facets from January 23rd to March 24th, 2020.
Fig. 2
Fig. 2
All facets of social distancing mapped for January 2020. Larger diameter denotes higher volume of tweets. Threshold = 5.
Fig. 3
Fig. 3
All facets of social distancing mapped for February 2020. Larger diameter denotes higher volume of tweets. Threshold = 8.
Fig. 4
Fig. 4
All facets of social distancing mapped for March 2020. Larger diameter denotes higher volume of tweets. Threshold = 60.
See this image and copyright information in PMC

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