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Paper Number
1558
Paper Type
Short
Abstract
Increasingly, individuals are seeking social support through online, which can significantly improve both mental and physical well-being. However, inadequate or misaligned support can be ineffective or even be harmful. This underscores the need for classifying the social support needs in health-related questions. Developing such a model requires substantial labeled data, which is often expensive to manually annotate. To address this issue, we develop a novel Answer-Enhanced Semi-Supervised Deep Learning (AESSDL) Approach. The AESSDL approach incorporates a dynamic 2D interaction kernel designed to capture complex interaction patterns between questions and answers, along with a quality-aware attention layer that assigns varying weights to multiple answers for each question. Extensive empirical analyses demonstrate that our method outperforms existing question classification techniques and alternative semi-supervised approaches. Practically, our approach enables online platforms to better understand user needs, facilitating timely and personalized interventions, such as recommending appropriate responders and relevant digital resources.
Recommended Citation
Kuang, Junwei; Yang, Liang; Cui, Shaoze; and Fan, Weiguo, "Understanding Social Support Needs in Health Question: An Answer-Enhanced Semi-Supervised Deep Learning Approach" (2024). ICIS 2024 Proceedings. 12.
https://aisel.aisnet.org/icis2024/general_is/general_is/12
Understanding Social Support Needs in Health Question: An Answer-Enhanced Semi-Supervised Deep Learning Approach
Increasingly, individuals are seeking social support through online, which can significantly improve both mental and physical well-being. However, inadequate or misaligned support can be ineffective or even be harmful. This underscores the need for classifying the social support needs in health-related questions. Developing such a model requires substantial labeled data, which is often expensive to manually annotate. To address this issue, we develop a novel Answer-Enhanced Semi-Supervised Deep Learning (AESSDL) Approach. The AESSDL approach incorporates a dynamic 2D interaction kernel designed to capture complex interaction patterns between questions and answers, along with a quality-aware attention layer that assigns varying weights to multiple answers for each question. Extensive empirical analyses demonstrate that our method outperforms existing question classification techniques and alternative semi-supervised approaches. Practically, our approach enables online platforms to better understand user needs, facilitating timely and personalized interventions, such as recommending appropriate responders and relevant digital resources.
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