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

Guocheng Feng; Huaiyu Cai; Wei Quan

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

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arxiv 2307.07558 v1 pith:YL5WVPWO submitted 2023-07-11 cs.SI cs.CL

Guocheng Feng , Huaiyu Cai , Wei Quan This is my paper · ORCID

classification cs.SIcs.CL

keywords covidlongmentalindividualsemotionalemotionswell-beingchallenges

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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Integrating Zero-Shot Classification to Advance Long COVID Literature: A Systematic Social Media-Centered Review
cs.SI 2024-12 reject novelty 2.0 of 10

A literature review uses zero-shot transformer classification to organize 40 Long COVID social media studies into four themes, with no external validation and no shared artifacts.

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