A Study of the Effects of the COVID-19 Pandemic on the Experience of Back Pain Reported on Twitter ® in the United States: A Natural Language Processing Approach

Krzysztof Fiok, Waldemar Karwowski, Edgar Gutierrez, Maham Saeidi, Awad M. Aljuaid, Mohammad Reza Davahli, Redha Taiar, Tadeusz Marek and Ben D. Sawyer
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Krzysztof Fiok: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
Waldemar Karwowski: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
Edgar Gutierrez: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
Maham Saeidi: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
Awad M. Aljuaid: Department of Industrial Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Mohammad Reza Davahli: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
Redha Taiar: MATIM, Université de Reims Champagne-Ardenne, 51100 Reims, France
Tadeusz Marek: Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-252 Kraków, Poland
Ben D. Sawyer: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA

IJERPH, 2021, vol. 18, issue 9, 1-14

Abstract: The COVID-19 pandemic has changed our lifestyles, habits, and daily routine. Some of the impacts of COVID-19 have been widely reported already. However, many effects of the COVID-19 pandemic are still to be discovered. The main objective of this study was to assess the changes in the frequency of reported physical back pain complaints reported during the COVID-19 pandemic. In contrast to other published studies, we target the general population using Twitter as a data source. Specifically, we aim to investigate differences in the number of back pain complaints between the pre-pandemic and during the pandemic. A total of 53,234 and 78,559 tweets were analyzed for November 2019 and November 2020, respectively. Because Twitter users do not always complain explicitly when they tweet about the experience of back pain, we have designed an intelligent filter based on natural language processing (NLP) to automatically classify the examined tweets into the back pain complaining class and other tweets. Analysis of filtered tweets indicated an 84% increase in the back pain complaints reported in November 2020 compared to November 2019. These results might indicate significant changes in lifestyle during the COVID-19 pandemic, including restrictions in daily body movements and reduced exposure to routine physical exercise.

Keywords: COVID-19 pandemics; back pain reports; Twitter; natural language processing (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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