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What Remains Now That the Fear Has Passed? Developmental Trajectory Analysis of COVID-19 Pandemic for Co-occurrences of Twitter, Google Trends, and Public Health Data

Published online by Cambridge University Press:  15 June 2023

Benjamin Havis Rathke ,
Han Yu Open the ORCID record for Han Yu [Opens in a new window]  and
Hong Huang
Benjamin Havis Rathke
Affiliation:
Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, Colorado, USA
Han Yu*
Affiliation:
Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, Colorado, USA
Hong Huang
Affiliation:
School of Information, University of South Florida, Tampa, Florida, USA
*
Corresponding author: Han Yu; Email: han.yu@unco.edu.
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Abstract

Objective:

The rapid onset of coronavirus disease 2019 (COVID-19) created a complex virtual collective consciousness. Misinformation and polarization were hallmarks of the pandemic in the United States, highlighting the importance of studying public opinion online. Humans express their thoughts and feelings more openly than ever before on social media; co-occurrence of multiple data sources have become valuable for monitoring and understanding public sentimental preparedness and response to an event within our society.

Methods:

In this study, Twitter and Google Trends data were used as the co-occurrence data for the understanding of the dynamics of sentiment and interest during the COVID-19 pandemic in the United States from January 2020 to September 2021. Developmental trajectory analysis of Twitter sentiment was conducted using corpus linguistic techniques and word cloud mapping to reveal 8 positive and negative sentiments and emotions. Machine learning algorithms were used to implement the opinion mining how Twitter sentiment was related to Google Trends interest with historical COVID-19 public health data.

Results:

The sentiment analysis went beyond polarity to detect specific feelings and emotions during the pandemic.

Conclusions:

The discoveries on the behaviors of emotions at each stage of the pandemic were presented from the emotion detection when associated with the historical COVID-19 data and Google Trends data.

Keywords

social mediatext miningsentiment analysisco-occurrenceCOVID-19
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e471
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Society for Disaster Medicine and Public Health

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