Accepted for/Published in: JMIR Formative Research
Date Submitted: Sep 7, 2024
Date Accepted: Jun 27, 2025
COVID-19 Symptoms Surveillance During the Omicron Variant Period: Sentiment Analysis of Twitter (X) Data in English
ABSTRACT
Background:
The global outbreak of COVID-19 has significantly impacted healthcare systems and has necessitated timely access to information for effective decision-making by healthcare authorities. Conventional methods for collecting patient data and analyzing virus mutations are resource-intensive. In the current era of rapid internet development, information on COVID-19 infections could be collected by a novel approach that leverages social media, particularly Twitter.
Objective:
The aim of this study was to analyze the trending patterns of tweets containing information about various COVID-19 symptoms, explore their synchronization and correlation with conventional monitoring data, and provide insights into the evolution of the virus. We categorized tweet sentiments to understand the predictive power of negative emotions of different symptoms in anticipating the emergence of new viral variants and offering real-time assistance to affected individuals.
Methods:
Relevant user tweets from 2022 containing information about COVID-19 symptoms were extracted from Twitter. We visualized tweet trends and examined their correlation with daily COVID-19 cases. The RoBERTa model for sentiment analysis was used to categorize tweets as negative, positive, or neutral. We explored changes in the progression of the virus by observing fluctuations in negative emotions associated with different symptoms. Real-time Twitter users with negative sentiments were geographically plotted.
Results:
The most prominent topics of discussion were fever, sore throat, and headache. The weekly average daily tweets exhibited different fluctuation patterns in different stages of sub-variants. Specifically, within the negative sentiment category, tweets about fever had the potential to change according to the sub-variants.
Conclusions:
This study underscores the potential of using social media, particularly tweet trends, for real-time analysis of COVID-19 infections and has demonstrated correlations with major symptoms. The degree of negative emotions expressed in tweets is valuable in predicting the emergence of new variants of COVID-19 and facilitating the provision of timely assistance to affected individuals.
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Copyright
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