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

2025-03-21

AI Summary

Identifying patients’ social support needs in online health Q&A communities remains challenging due to severe scarcity of labeled data and extreme class imbalance. Method: This study proposes HA-SOS, a novel framework integrating answer-guided semi-supervised learning, reliability- and diversity-aware large language model (LLM)-based data augmentation, and a unified automatic labeling training mechanism. HA-SOS systematically embodies the computational design science paradigm to enable end-to-end optimization for text classification. Contribution/Results: Extensive experiments demonstrate that HA-SOS significantly outperforms state-of-the-art question classification and semi-supervised baselines across key metrics—including accuracy, macro-F1, and minority-class recall—thereby enabling platforms to deliver precise, timely, and personalized social support responses.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Natural Language Processing: Question Answering
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Web question answering
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Community question answering

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.

Problem

• Identify social support needs in health questions
• Address scarcity and imbalance in labeled data
• Improve question classification for personalized responses

Innovation

• Semi-supervised learning for social support classification
• LLM-based data augmentation with reliability mechanisms
• Unified training process for automatic question labeling