Purpose

This paper aims to investigate user health information seeking and sharing patterns and content in an online diabetes community and explore the similarities and differences in the ways and themes they expressed.

Design/methodology/approach

Multiple methods are applied to analyze the expressions and themes that users seek and share based on large-scale text data in an online diabetes community. First, a text classifier using deep learning method is performed based on the expression category this study developed. Second, statistical and social network analyses are used to measure the popularity and compare differences between expressions. Third, topic modeling, manual coding and similarity analysis are used to mining topics and thematic similarity between seeking and sharing threads.

Findings

There are four different ways users seek and share in online health communities (OHCs) including informational seeking, situational seeking, objective information sharing and experiential information sharing. The results indicate that threads with self-disclosure could receive more replies and attract more users to contribute. This study also examines the 10 topics that were discussed for information seeking and 14 topics for information sharing. They shared three discussion themes: self-management, medication and symptoms. Information about symptoms can be largely matched between seeking and sharing threads while there is less overlap in self-management and medication categories.

Originality/value

Being different from previous studies that mainly describe one type of health information behavior, this paper analyzes user health information seeking and sharing behaviors in OHCs and investigates whether there is a correspondence or discrepancy between expressions and information users spontaneously seek and share in OHCs.

中文翻译：

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用户在在线糖尿病社区中寻求和分享的内容：检查表达和主题的异同

目的

本文旨在调查在线糖尿病社区中用户健康信息搜索和共享模式和内容，并探讨他们表达方式和主题的异同。

设计/方法/方法

基于在线糖尿病社区中的大规模文本数据，应用多种方法分析用户寻求和分享的表达和主题。首先，基于本研究开发的表达类别执行使用深度学习方法的文本分类器。其次，使用统计和社交网络分析来衡量流行度并比较表达之间的差异。第三，主题建模、人工编码和相似性分析用于挖掘主题和寻找和共享线程之间的主题相似性。

发现

用户在在线健康社区 (OHC) 中寻求和分享的方式有四种，包括信息寻求、情境寻求、客观信息共享和体验信息共享。结果表明，具有自我披露的线程可以收到更多的回复并吸引更多的用户参与。本研究还检查了讨论的 10 个信息搜索主题和 14 个信息共享主题。他们分享了三个讨论主题：自我管理、药物治疗和症状。有关症状的信息可以在搜索和共享线程之间大致匹配，而自我管理和药物类别的重叠较少。

原创性/价值

有别于以往主要描述一种健康信息行为的研究，本文分析了用户健康信息在 OHCs 中的搜索和共享行为，并考察了用户在 OHCs 中自发搜索和共享的信息表达与信息是否存在对应或差异。