Thakur, N. Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis. Appl. Syst. Innov.2023, 6, 92.
Thakur, N. Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis. Appl. Syst. Innov. 2023, 6, 92.
Thakur, N. Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis. Appl. Syst. Innov.2023, 6, 92.
Thakur, N. Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis. Appl. Syst. Innov. 2023, 6, 92.
Abstract
Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.
Keywords
COVID-19; long COVID; social media; Twitter; big data; data analysis; natural Language processing; data science; sentiment analysis
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
