Exploring the Emotional and Mental Well-Being of Individuals with Long COVID Through Twitter Analysis

TL;DR

This study analyzes Twitter data to understand the emotional and mental well-being of Long COVID patients, revealing dominant negative emotions and identifying key concerns to inform mental health support strategies.

Contribution

It introduces a novel Twitter-based analysis method to classify emotions and topics related to Long COVID, highlighting emotional trends and concerns.

Findings

Negative emotions dominated throughout the study period.

Two emotional peaks aligned with COVID variant outbreaks.

Insights support targeted mental health interventions.

Abstract

The COVID-19 pandemic has led to the emergence of Long COVID, a cluster of symptoms that persist after infection. Long COVID patients may also experience mental health challenges, making it essential to understand individuals' emotional and mental well-being. This study aims to gain a deeper understanding of Long COVID individuals' emotional and mental well-being, identify the topics that most concern them, and explore potential correlations between their emotions and social media activity. Specifically, we classify tweets into four categories based on the content, detect the presence of six basic emotions, and extract prevalent topics. Our analyses reveal that negative emotions dominated throughout the study period, with two peaks during critical periods, such as the outbreak of new COVID variants. The findings of this study have implications for policy and measures for addressing the mental health challenges of individuals with Long COVID and provide a foundation for future work.