结合不同言语线索以改进基于文本的计算机交付健康信息传递
Incorporating Different Verbal Cues to Improve Text-Based Computer-Delivered Health Messaging
AI总结：
本文针对人机数字健康信息传递中计算机代理难以恰当展现人类对话微妙之处的问题，通过三项研究探索如何生成健康信息、选择合适的对话风格与正式程度以及优化信息格式，从而创建更有效的数字健康干预措施。
摘要：
智能手机的普及推动了按需医疗保健供应的增加。例如，人们可以与处境相似的人分享与疾病相关的经验，医疗保健专家也可以为可治愈的疾病、绝症和精神疾病提供更好的治疗和护理建议。除了这种人与人之间的交流，人与计算机之间的数字健康信息传递（如聊天机器人）的使用也日益增加。这具有优势，因为它们能提供同步且匿名的反馈，而无需人类对话伙伴。然而，人类对话中存在许多计算机代理可能无法恰当展现的微妙之处。例如，需要为对话选择合适的对话风格、礼仪、礼貌策略或共情反应。令人鼓舞的是，“计算机是社会参与者”（CASA）假设认为，人们对计算机应用与对人相同的社会规范。基于此，以往的研究侧重于将对话策略应用于计算机代理，使其体现更理想的人类特征。然而，如果计算机代理在这方面失败，可能会引起用户的负面反应。因此，在这篇论文中，我们描述了一系列研究，以实现更有效的人机数字健康信息传递。在我们的第一项研究中，我们使用众包平台来生成健康信息。我们的第二项研究调查了健康聊天机器人的对话风格的影响。在最后一项研究中，我们调查了聊天机器人在引用用户先前话语时使用的格式。总而言之，我们研究了如何创建更有效的数字健康干预措施，从生成健康信息，到选择合适的信息正式程度，最后到格式化引用用户先前话语的信息。
英文摘要：
The ubiquity of smartphones has led to an increase in on demand healthcare being supplied. For example, people can share their illness-related experiences with others similar to themselves, and healthcare experts can offer advice for better treatment and care for remediable, terminal and mental illnesses. As well as this human-to-human communication, there has been an increased use of human-to-computer digital health messaging, such as chatbots. These can prove advantageous as they offer synchronous and anonymous feedback without the need for a human conversational partner. However, there are many subtleties involved in human conversation that a computer agent may not properly exhibit. For example, there are various conversational styles, etiquettes, politeness strategies or empathic responses that need to be chosen appropriately for the conversation. Encouragingly, computers are social actors (CASA) posits that people apply the same social norms to computers as they would do to people. On from this, previous studies have focused on applying conversational strategies to computer agents to make them embody more favourable human characteristics. However, if a computer agent fails in this regard it can lead to negative reactions from users. Therefore, in this dissertation we describe a series of studies we carried out to lead to more effective human-to-computer digital health messaging. In our first study, we use the crowd [...] Our second study investigates the effect of a health chatbot's conversational style [...] In our final study, we investigate the format used by a chatbot when [...] In summary, we have researched how to create more effective digital health interventions starting from generating health messages, to choosing an appropriate formality of messaging, and finally to formatting messages which reference a user's previous utterances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
