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

• URL: http://arxiv.org/abs/2503.11845v1
• Date: Fri, 14 Mar 2025 20:13:08 GMT
• Title: Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework
• Authors: Nirmalya Thakur, Niven Francis Da Guia Fernandes, Madje Tobi Marc'Avent Tchona,
• Abstract summary: Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment.<n>We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews.<n>Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Decade of Natural Language Processing in Chronic Pain: A Systematic Review [0.0]
  Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines.<n>This review aims to investigate the state of the research on NLP-based interventions for chronic pain research.
  arXiv  Detail & Related papers  (2024-12-19T19:46:09Z)
• Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study [4.769236554995528]
  We propose a retrieval-augmented generation architecture for medical question answering on emerging issues associated with health-related topics.<n>Our framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data.<n>Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO.
  arXiv  Detail & Related papers  (2024-05-29T20:56:52Z)
• SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge [63.311045291016555]
  Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts. This paper summarizes the challenging task, data, and research progress.
  arXiv  Detail & Related papers  (2024-05-17T02:36:14Z)
• Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts [0.0]
  We use Longformer to handle the problem of finding causal indicators behind mental illness in self-reported text. Experiments show that Longformer achieves new state-of-the-art results on M-CAMS, a publicly available dataset with 62% F1-score. We believe our work facilitates causal analysis of depression and suicide risk on social media data, and shows potential for application on other mental health conditions.
  arXiv  Detail & Related papers  (2023-04-08T22:44:32Z)
• Theme-driven Keyphrase Extraction to Analyze Social Media Discourse [3.2365983191405103]
  This paper introduces a theme-driven keyphrase extraction framework tailored for social media. We develop a novel data collection and curation framework for theme-driven keyphrase extraction. We create MOUD-Keyphrase, the first dataset of its kind comprising human-annotated keyphrases from a Reddit community.
  arXiv  Detail & Related papers  (2023-01-27T03:00:46Z)
• Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
  This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
  arXiv  Detail & Related papers  (2022-11-14T18:47:26Z)
• Aggression and "hate speech" in communication of media users: analysis of control capabilities [50.591267188664666]
  Authors studied the possibilities of mutual influence of users in new media. They found a high level of aggression and hate speech when discussing an urgent social problem - measures for COVID-19 fighting. Results can be useful for developing media content in a modern digital environment.
  arXiv  Detail & Related papers  (2022-08-25T15:53:32Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)
• Beyond Social Media Analytics: Understanding Human Behaviour and Deep Emotion using Self Structuring Incremental Machine Learning [1.2487990897680423]
  This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition. Two platforms were built to capture insights from fast-paced and slow-paced social data.
  arXiv  Detail & Related papers  (2020-09-05T14:53:26Z)
• Digital personal health libraries: a systematic literature review [15.392783869176778]
  This paper gives context on recent literature regarding the development of digital personal health libraries (PHL) It provides insights into the potential application of consumer health informatics in diverse clinical specialties.
  arXiv  Detail & Related papers  (2020-06-21T01:11:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.