Conference proceeding
Understanding Social Support Needs in Health Question: An Answer-Enhanced Semi-Supervised Deep Learning Approach
45th International Conference on Information Systems, ICIS 2024
2024
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.
Details
- Title: Subtitle
- Understanding Social Support Needs in Health Question: An Answer-Enhanced Semi-Supervised Deep Learning Approach
- Creators
- Junwei Kuang - Beijing Institute of TechnologyLiang Yang - Beijing Institute of TechnologyShaoze Cui - Beijing Institute of TechnologyWeiguo Fan - The University of Iowa, IA, United States
- Resource Type
- Conference proceeding
- Publication Details
- 45th International Conference on Information Systems, ICIS 2024
- Grant note
- GZB20230934 / Intelligence Community Postdoctoral Research Fellowship Program (100011040) 72110107003 / National Natural Science Foundation of China (501100001809) China Postdoctoral Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/501100002858) 2023M740237 / China Postdoctoral Science Foundation (501100002858) National Natural Science Foundation of China (http://data.elsevier.com/vocabulary/SciValFunders/501100001809) 2023M740237 / China Postdoctoral Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/501100002858) GZB20230934 / CPSF 72110107003 / National Natural Science Foundation of China (http://data.elsevier.com/vocabulary/SciValFunders/501100001809)
- Language
- English
- Date published
- 2024
- Academic Unit
- Business Analytics
- Record Identifier
- 9984865308202771
Metrics
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