Understanding Social Support Needs in Questions: A Hybrid Approach Integrating Semi-Supervised Learning and LLM-based Data Augmentation

Junwei Kuang; Liang Yang; Shaoze Cui; Weiguo Fan

doi:10.48550/arxiv.2503.17421

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Understanding Social Support Needs in Questions: A Hybrid Approach Integrating Semi-Supervised Learning and LLM-based Data Augmentation

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Understanding Social Support Needs in Questions: A Hybrid Approach Integrating Semi-Supervised Learning and LLM-based Data Augmentation

Junwei Kuang, Liang Yang, Shaoze Cui and Weiguo Fan

ArXiV.org

Cornell University

03/21/2025

DOI: 10.48550/arxiv.2503.17421

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https://doi.org/10.48550/arxiv.2503.17421View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Patients are increasingly turning to online health Q&A communities for social support to improve their well-being. However, when this support received does not align with their specific needs, it may prove ineffective or even detrimental. This necessitates a model capable of identifying the social support needs in questions. However, training such a model is challenging due to the scarcity and class imbalance issues of labeled data. To overcome these challenges, we follow the computational design science paradigm to develop a novel framework, Hybrid Approach for SOcial Support need classification (HA-SOS). HA-SOS integrates an answer-enhanced semi-supervised learning approach, a text data augmentation technique leveraging large language models (LLMs) with reliability- and diversity-aware sample selection mechanism, and a unified training process to automatically label social support needs in questions. Extensive empirical evaluations demonstrate that HA-SOS significantly outperforms existing question classification models and alternative semi-supervised learning approaches. This research contributes to the literature on social support, question classification, semi-supervised learning, and text data augmentation. In practice, our HA-SOS framework facilitates online Q&A platform managers and answerers to better understand users' social support needs, enabling them to provide timely, personalized answers and interventions.

Computer Science - Artificial Intelligence

Computer Science - Computation and Language

Computer Science - Computers and Society

Details

Title: Subtitle: Understanding Social Support Needs in Questions: A Hybrid Approach Integrating Semi-Supervised Learning and LLM-based Data Augmentation
Creators: Junwei Kuang - Beijing Institute of Technology
Liang Yang - Beijing Institute of Technology
Shaoze Cui - Beijing Institute of Technology
Weiguo Fan - University of Iowa
Resource Type: Preprint
Publication Details: ArXiV.org
DOI: 10.48550/arxiv.2503.17421
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 03/21/2025
Academic Unit: Business Analytics
Record Identifier: 9984802194002771

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