Content-based social network analysis methods for data-driven health promotion

 描述(由申请者提供)：社会关系对健康、行为改变和慢性病管理的影响已经有了很好的记录。来自不同学科的研究人员试图了解和利用这些社会关系在健康促进中的作用。将点对点交流数字化的在线社区为研究人员提供了一个独特的机会，让他们了解人类行为变化的潜在机制。数字交流平台的出现导致调查人员质疑现有的行为改变理论的使用，这些理论是在面对面交流的背景下制定的。在线社区中社会行为结构的表现形式通常嵌入到用户交换的消息的内容中。大多数关于线上和线下社交网络的研究在试图辨别网络影响力的理论基础时，没有考虑点对点互动的传播内容。在拟议的研究中，我们将开发一个框架，将消息内容纳入基于网络的社会影响模型中。虽然普遍适用，但这些方法将基于试图戒烟的个人在在线社区QuitNet中的交流而开发，QuitNet是第一个在线戒烟社交网络。这些方法将被扩展到分析治疗日记上管理慢性病的成员之间的交流，这是一个在线社区，患者在这里分享他们日常的疾病管理经验。将定性方法与自动文本分析相结合，我们的工作将促进在线社交网络中用户交流的高通量实证分析，从而识别紧急用户需求和社区文化。将开发捕捉与健康行为变化和慢性病管理相关的认知因素演变的时间模型。将采用从属关系暴露模型和指数随机图建模技术来研究网络影响和依赖的特定内容的传播特征。这项研究提案将导致1)将通信内容纳入社会影响的网络模型的新方法，以增强我们对数字通信平台中行为变化的理论根源的理解；2)可扩展的技术，以根据通信内容和结构对用户交互的时间变化和网络依赖性进行建模；以及3)关于开发数字干预和技术功能的新建议，以利用社会联系来支持从事健康行为变化和慢性病管理的个人。

 DESCRIPTION (provided by applicant): The effects of social relationships on health behavior change and management of chronic conditions has been well documented. Investigators from a variety of disciplines have attempted to understand and harness the role of these social ties in health promotion. Online communities, which digitize peer-to-peer communication, provide a unique opportunity to researchers to understand the mechanisms underlying human behavior change. The onset of digital communication platforms has led investigators to question the use of existing behavior change theories that were formulated in the context of face-to-face communication. Manifestations of socio-behavioral constructs in online communities are often embedded in the content of the messages exchanged by users. Most studies of online and offline social networks have not considered communication content of peer-to-peer interactions when attempting to discern the theoretical underpinnings of network influence. In the proposed research, we will develop a framework for the incorporation of message content into network-based models of social influence. While generally applicable, the methods will be developed based on communications among individuals attempting to quit smoking in an online community, QuitNet, the first online social network for smoking cessation. The methods will be extended to analyze communication among members managing chronic conditions on TreatmentDiaries, an online community where patients share their daily experiences of disease management. Combining qualitative methods with automated text analysis, our work will facilitate high-throughput empirical analysis of user communication in online social networks allowing the identification of emergent user needs and community culture. Temporal models that capture the evolution of cognitive factors pertinent to health behavior change and chronic disease management will be developed. Content-specific communication characteristics of network influence and dependencies will be studied by adapting affiliation exposure models and exponential random graph modeling techniques. This research proposal will result in 1) novel methods to incorporate communication content into network models of social influence to enhance our understanding of the theoretical roots of behavior change in digital communication platforms; 2) scalable techniques to model temporal changes and network dependencies of user interactions in terms of communication content and structure; and 3) new proposals for the development of digital interventions and technology features that harness social ties to support individuals engaging in health behavior change and chronic disease management.