CALLM：通过移动日记与上下文感知语言模型理解癌症幸存者的情绪及干预时机
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models
AI总结：
本文提出上下文感知框架CALLM，结合大语言模型与检索增强生成分析癌症幸存者的移动日记，以理解其情绪状态并预测情绪调节意愿和干预可用性，从而实现个性化即时支持。
摘要：
癌症幸存者面临独特的情绪挑战，这些挑战影响其生活质量。移动日记条目为追踪情绪状态、提升自我觉察并促进健康结果提供了一种有前景的方法。本文旨在通过移动日记理解癌症幸存者的情绪状态以及与即时干预时机相关的关键变量，包括调节情绪的意愿和参与干预的可用性。尽管情绪分析工具在从文本中识别情绪方面显示出潜力，但当前方法缺乏理解简短移动日记叙述所必需的上下文理解能力。我们对癌症幸存者（N=407）日记条目的分析揭示了所描述情境与情绪状态之间的系统性关系：行政和健康相关情境与负面情绪及调节需求相关，而休闲活动则促进积极情绪。我们提出CALLM，一个上下文感知框架，利用大语言模型（LLMs）与检索增强生成（RAG），通过整合检索到的同伴经历和个人日记历史来分析这些简短条目。CALLM表现出强劲性能，平衡准确率分别达到：积极情绪72.96%、消极情绪73.29%、情绪调节意愿73.72%、干预可用性60.09%，优于语言模型基线。事后分析表明，模型置信度能强烈预测准确率，较长的日记条目通常能提升性能，而短暂的个性化阶段可带来有意义的改进。我们的发现证明了移动日记中的上下文信息如何被有效利用，以理解情绪体验、预测关键状态，并为个性化即时支持识别最佳干预时刻。
英文摘要：
Cancer survivors face unique emotional challenges that impact their quality of life. Mobile diary entries provide a promising method for tracking emotional states, improving self-awareness, and promoting well-being outcome. This paper aims to, through mobile diaries, understand cancer survivors' emotional states and key variables related to just-in-time intervention opportunities, including the desire to regulate emotions and the availability to engage in interventions. Although emotion analysis tools show potential for recognizing emotions from text, current methods lack the contextual understanding necessary to interpret brief mobile diary narratives. Our analysis of diary entries from cancer survivors (N=407) reveals systematic relationships between described contexts and emotional states, with administrative and health-related contexts associated with negative affect and regulation needs, while leisure activities promote positive emotions. We propose CALLM, a Context-Aware framework leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to analyze these brief entries by integrating retrieved peer experiences and personal diary history. CALLM demonstrates strong performance with balanced accuracies reaching 72.96% for positive affect, 73.29% for negative affect, 73.72% for emotion regulation desire, and 60.09% for intervention availability, outperforming language model baselines. Post-hoc analysis reveals that model confidence strongly predicts accuracy, with longer diary entries generally enhancing performance, and brief personalization periods yielding meaningful improvements. Our findings demonstrate how contextual information in mobile diaries can be effectively leveraged to understand emotional experiences, predict key states, and identify optimal intervention moments for personalized just-in-time support.
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。
