Content-Sensitive Characterization of Peer Interactions of Highly Engaged Users in an Online Community for Smoking Cessation: Mixed-Methods Approach for Modeling User Engagement in Health Promotion Interventions.

Q2 Medicine
Sahiti Myneni, Vishnupriya Sridharan, Nathan Cobb, Trevor Cohen
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引用次数: 3

Abstract

Background: Online communities provide affordable venues for behavior change. However, active user engagement holds the key to the success of these platforms. In order to enhance user engagement and in turn, health outcomes, it is essential to offer targeted interventional and informational support.

Objective: In this paper, we describe a content plus frequency framework to enable the characterization of highly engaged users in online communities and study theoretical techniques employed by these users through analysis of exchanged communication.

Methods: We applied the proposed methodology for analysis of peer interactions within QuitNet, an online community for smoking cessation. Firstly, we identified 144 highly engaged users based on communication frequency within QuitNet over a period of 16 years. Secondly, we used the taxonomy of behavior change techniques, text analysis methods from distributional semantics, machine learning, and sentiment analysis to assign theory-driven labels to content. Finally, we extracted content-specific insights from peer interactions (n=159,483 messages) among highly engaged QuitNet users.

Results: Studying user engagement using our proposed framework led to the definition of 3 user categories-conversation initiators, conversation attractors, and frequent posters. Specific behavior change techniques employed by top tier users (threshold set at top 3) within these 3 user groups were found to be goal setting, social support, rewards and threat, and comparison of outcomes. Engagement-specific trends within sentiment manifestations were also identified.

Conclusions: Use of content-inclusive analytics has offered deep insight into specific behavior change techniques employed by highly engaged users within QuitNet. Implications for personalization and active user engagement are discussed.

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戒烟在线社区中高度参与用户的同伴互动的内容敏感特征:健康促进干预中用户参与建模的混合方法
背景:在线社区为行为改变提供了负担得起的场所。然而,活跃的用户粘性是这些平台成功的关键。为了提高用户参与度,进而提高健康成果,必须提供有针对性的干预和信息支持。目的:在本文中,我们描述了一个内容加频率框架,以实现在线社区中高度参与用户的特征,并通过分析交换通信来研究这些用户使用的理论技术。方法:我们应用提出的方法来分析戒烟在线社区QuitNet内的同伴互动。首先,我们根据QuitNet在16年间的通信频率确定了144名高度参与的用户。其次,我们使用行为改变技术的分类法、来自分布语义的文本分析方法、机器学习和情感分析来为内容分配理论驱动的标签。最后,我们从高度活跃的QuitNet用户的同伴互动(n=159,483条消息)中提取了特定内容的见解。结果:使用我们提出的框架研究用户参与度导致了3种用户类别的定义——对话发起者、对话吸引者和频繁发帖者。在这3个用户群体中,顶级用户(阈值设置在前3位)采用的具体行为改变技术包括目标设定、社会支持、奖励和威胁以及结果比较。还确定了情绪表现中特定于参与的趋势。结论:内容包容性分析的使用为QuitNet中高参与度用户所采用的特定行为改变技术提供了深入的见解。对个性化和用户积极参与的影响进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Participatory Medicine
Journal of Participatory Medicine Medicine-Medicine (miscellaneous)
CiteScore
3.20
自引率
0.00%
发文量
8
审稿时长
12 weeks
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