Computer Science Department, King Khalid University, Abha, Saudi Arabia.
Center for Artificial Intelligence, King Khalid University, Abha, Saudi Arabia.
J Med Internet Res. 2024 Sep 12;26:e48257. doi: 10.2196/48257.
Health information consumers increasingly rely on question-and-answer (Q&A) communities to address their health concerns. However, the quality of questions posted significantly impacts the likelihood and relevance of received answers.
This study aims to improve our understanding of the quality of health questions within web-based Q&A communities.
We develop a novel framework for defining and measuring question quality within web-based health communities, incorporating content- and language-based variables. This framework leverages k-means clustering and establishes automated metrics to assess overall question quality. To validate our framework, we analyze questions related to kidney disease from expert-curated and community-based Q&A platforms. Expert evaluations confirm the validity of our quality construct, while regression analysis helps identify key variables.
High-quality questions were more likely to include demographic and medical information than lower-quality questions (P<.001). In contrast, asking questions at the various stages of disease development was less likely to reflect high-quality questions (P<.001). Low-quality questions were generally shorter with lengthier sentences than high-quality questions (P<.01).
Our findings empower consumers to formulate more effective health information questions, ultimately leading to better engagement and more valuable insights within web-based Q&A communities. Furthermore, our findings provide valuable insights for platform developers and moderators seeking to enhance the quality of user interactions and foster a more trustworthy and informative environment for health information exchange.
健康信息消费者越来越依赖问答（Q&A）社区来解决他们的健康问题。然而，发布的问题的质量显著影响了收到答案的可能性和相关性。
本研究旨在增进我们对网络问答社区中健康问题质量的理解。
我们开发了一个新的框架，用于定义和衡量网络健康社区中的问题质量，结合了内容和语言变量。该框架利用 K-均值聚类并建立自动指标来评估整体问题质量。为了验证我们的框架，我们分析了来自专家策划和社区的 Q&A 平台的与肾病相关的问题。专家评估证实了我们的质量构建的有效性，而回归分析有助于确定关键变量。
高质量的问题更有可能包含人口统计学和医疗信息，而低质量的问题则较少包含（P<.001）。相比之下，在疾病发展的各个阶段提出问题不太可能反映出高质量的问题（P<.001）。低质量的问题通常比高质量的问题更简短，句子也更长（P<.01）。
我们的研究结果使消费者能够更有效地提出健康信息问题，最终在网络问答社区中实现更好的参与度和更有价值的见解。此外，我们的研究结果为寻求提高用户互动质量和促进更值得信赖和信息丰富的健康信息交流环境的平台开发人员和管理员提供了有价值的见解。
