CHS: Medium: Understanding and designing for online disclosure and its effects on well-being

从日常生活的平凡细节到计划自杀的悲惨警告，人们在社交媒体上交换大量的个人信息，这违背了传统的自我披露模式。该项目将促进对社交媒体环境中个人信息披露的理解，并将利用这些信息制定旨在增强自我反思和有效寻求和提供支持的能力的福祉干预措施。这种干预措施使个人和整个社会受益，帮助人们了解如何以及从何处在非正式和正式社交网络中寻求支持。了解自我披露的关键驱动因素也有助于改进使用个人数据的其他系统的设计，并将为公众关于在线个人信息使用和风险的讨论提供信息。PI将通过用户参与实验、使用系统和在研究界以外宣传研究成果，努力提高对披露利益和风险的认识，例如，通过康奈尔大学布朗芬布伦纳转化研究中心将研究成果转化为实际应用。更具体地说，这项研究将产生一个多理论和多层次的模型，说明个人属性（如个性和心理健康状况），技术启示（如可审查性和受众可见性）以及社交网络属性（如大小，密度，多样性和联系强度），单独或组合地影响披露和披露战略的预期回报和风险。这些模型将把网络成员的反应纳入披露的制作周期，并研究这些综合特征如何决定预期和实际结果。研究人员将收集文本和照片中的实际披露行为的例子，并注释有关人们的披露目标和感知，个人特征和社交网络的信息，并将使用这些来验证社交媒体数据中披露的存在和反应的预测模型。这些模型将有助于识别人们何时创建有意义的内容，这些内容反过来又可用于支持社会媒体用户福祉的系统和干预措施，包括普通人群和风险人群。这些系统和干预措施将通过使用披露的信息来促进反思，丰富现有的积极心理学干预措施，并促进对披露和心理健康需求的认识和有效应对。

From mundane details of daily life to tragic warnings of planned suicide, people exchange a massive amount of personal information in social media that defies traditional models of self-disclosure. This project will advance understanding of personal information disclosure in social media contexts and will use this information to develop well-being interventions designed to enhance self-reflection and capacity to productively seek and offer support. Such interventions benefit individuals and society overall by helping people know how and from where to seek support in informal and formal social networks. Knowledge about key drivers of self-disclosure will also be useful in improving the design of other systems that use personal data and will inform public discussions about the use and risks of personal information online. The PIs will work to raise awareness of disclosure benefits and risks through users' participation in experiments, use of systems, and publicizing research results beyond the research community, e.g., by working to move research results into practical applications through the Bronfenbrenner Center for Translational Research at Cornell.More specifically, this research will yield a multi-theoretical and multi-level model of how individual attributes such as personality and mental health status, technological affordances such as reviewability and audience visibility, and social network properties such as size, density, diversity, and tie strength, individually and in combination, shape anticipated rewards and risks of disclosure and disclosure strategies. These models will incorporate responses by network members into the production cycle of disclosure, and examine how these combined characteristics determine both anticipated and actual outcomes. The researchers will collect examples of actual disclosure behaviors in both text and photos, annotated with information about people's disclosure goals and perceptions, individual characteristics, and social networks, and will use these to validate predictive models of the presence of and responses to disclosure in social media data. These models will help identify when people create meaningful content, which in turn can be used in systems and interventions that support the well-being of social media users, both in the general population and those at risk. These systems and interventions will operate by using disclosed information to facilitate reflection, enrich existing positive psychology interventions, and promote awareness of and effective responses to disclosure and mental health needs.