基于深度学习和聚类的主题一致性建模框架,实现卫生信息供需匹配

IF 2.8 2区 管理学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Dongxiao Gu, Hu Liu, Huimin Zhao, Xuejie Yang, Min Li, Changyong Liang
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引用次数: 0

摘要

通过卫生信息传播提高健康素养是改善人口健康状况最经济、最有效的机制之一。这一过程需要充分考虑健康信息供需的主题适宜性,降低信息过载和供需错配对健康信息获取积极性的影响。我们提出了一个健康信息主题建模分析框架,该框架整合了深度学习方法和聚类技术,对健康信息的供需双方主题进行建模,并量化供需双方的主题契合度。为了验证该框架的有效性,我们对来自两个著名社交网络平台的 90,418 条文本数据集进行了实证分析。结果表明,总体而言,健康信息的供给尚未满足需求,健康信息的需求尚未在相当程度上得到满足,尤其是与疾病相关的话题,而且同一健康话题的供需双方存在明显的不一致性。公共卫生决策部门和内容生产者可以根据确定的卫生主题分布情况,调整信息选择和传播策略,从而提高公共卫生信息传播的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep learning and clustering-based topic consistency modeling framework for matching health information supply and demand

Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply-side and demand-side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease-related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy-making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.

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来源期刊
CiteScore
8.30
自引率
8.60%
发文量
115
期刊介绍: The Journal of the Association for Information Science and Technology (JASIST) is a leading international forum for peer-reviewed research in information science. For more than half a century, JASIST has provided intellectual leadership by publishing original research that focuses on the production, discovery, recording, storage, representation, retrieval, presentation, manipulation, dissemination, use, and evaluation of information and on the tools and techniques associated with these processes. The Journal welcomes rigorous work of an empirical, experimental, ethnographic, conceptual, historical, socio-technical, policy-analytic, or critical-theoretical nature. JASIST also commissions in-depth review articles (“Advances in Information Science”) and reviews of print and other media.
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