Junwei Kuang

Junwei Kuang, Liang Yang, Shaoze Cui et al.

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.

Junwei Kuang, Jiaheng Xie, Zhijun Yan

The early identification and intervention of latent depression are of significant societal importance for mental health governance. While current automated detection methods based on social media have shown progress, their decision-making processes often lack a clinically interpretable framework, particularly in capturing the duration and dynamic evolution of depressive symptoms. To address this, this study introduces a semantic parsing network integrated with multi-scale temporal prototype learning. The model detects depressive states by capturing temporal patterns and semantic prototypes in users' emotional expression, providing a duration-aware interpretation of underlying symptoms. Validated on a large-scale social media dataset, the model outperforms existing state-of-the-art methods. Analytical results indicate that the model can identify emotional expression patterns not systematically documented in traditional survey-based approaches, such as sustained narratives expressing admiration for an "alternative life." Further user evaluation demonstrates the model's superior interpretability compared to baseline methods. This research contributes a structurally transparent, clinically aligned framework for depression detection in social media to the information systems literature. In practice, the model can generate dynamic emotional profiles for social platform users, assisting in the targeted allocation of mental health support resources.