Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework

Fuente: arXiv

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Bibliographic Details
Main Authors:	Thakur, Nirmalya, Fernandes, Niven Francis Da Guia, Tchona, Madje Tobi Marc'Avent
Format:	Preprint
Published:	2025
Subjects:	Social and Information Networks Computers and Society Machine Learning I.2.7; I.2.8; I.5.4; K.4.2; H.2.8; I.2.6
Online Access:	Acceder al recurso
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_version_	1866910876818210816
author	Thakur, Nirmalya Fernandes, Niven Francis Da Guia Tchona, Madje Tobi Marc'Avent
author_facet	Thakur, Nirmalya Fernandes, Niven Francis Da Guia Tchona, Madje Tobi Marc'Avent
contents	Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID. This paper examines recent works focusing on mining, analyzing, and interpreting user-generated content on social media platforms to capture the broader discourse on persistent post-COVID conditions. A novel transformer-based zero-shot learning approach serves as the foundation for classifying research papers in this area into four primary categories: Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy Advocacy or Public Health Communication, and Online Communities and Social Support. This methodology achieved an average confidence of 0.7788, with the minimum and maximum confidence being 0.1566 and 0.9928, respectively. This model showcases the ability of advanced language models to categorize research papers without any training data or predefined classification labels, thus enabling a more rapid and scalable assessment of existing literature. This paper also highlights the multifaceted nature of Long COVID research by demonstrating how advanced computational techniques applied to social media conversations can reveal deeper insights into the experiences, symptoms, and narratives of individuals affected by Long COVID.
format	Preprint
id	arxiv_https___arxiv_org_abs_2503_11845
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework Thakur, Nirmalya Fernandes, Niven Francis Da Guia Tchona, Madje Tobi Marc'Avent Social and Information Networks Computers and Society Machine Learning I.2.7; I.2.8; I.5.4; K.4.2; H.2.8; I.2.6 Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID. This paper examines recent works focusing on mining, analyzing, and interpreting user-generated content on social media platforms to capture the broader discourse on persistent post-COVID conditions. A novel transformer-based zero-shot learning approach serves as the foundation for classifying research papers in this area into four primary categories: Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy Advocacy or Public Health Communication, and Online Communities and Social Support. This methodology achieved an average confidence of 0.7788, with the minimum and maximum confidence being 0.1566 and 0.9928, respectively. This model showcases the ability of advanced language models to categorize research papers without any training data or predefined classification labels, thus enabling a more rapid and scalable assessment of existing literature. This paper also highlights the multifaceted nature of Long COVID research by demonstrating how advanced computational techniques applied to social media conversations can reveal deeper insights into the experiences, symptoms, and narratives of individuals affected by Long COVID.
title	Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework
topic	Social and Information Networks Computers and Society Machine Learning I.2.7; I.2.8; I.5.4; K.4.2; H.2.8; I.2.6
url	https://arxiv.org/abs/2503.11845