人与机器人、虚拟助手对话时，自我披露内容和语义基本一致。
Comparing Self-Disclosure Themes and Semantics to a Human, a Robot, and a Disembodied Agent
- 用句子嵌入和聚类分析对话主题，由大模型标注解释。
- 三类对话中主题分布无显著差异，语义表达高度相似。
- 适合关注人机交互中情感表达一致性的人看。
随着社交机器人等人工代理的对话能力增强，理解其具身形态是否影响自我披露的内容与意义至关重要。本研究分析了三项受控实验中的对话数据，参与者分别向真人、类人社交机器人及无具身对话代理进行自我披露。通过句子嵌入与聚类方法识别披露主题，并由大语言模型进行标注与解释。结果表明：主题分布在不同具身条件下无显著差异，语义相似性分析显示披露表达方式高度一致。这说明尽管具身可能影响人际互动行为，但人们在与人类或人工对话对象交流时，仍保持稳定的主题聚焦与语义结构。
原文摘要 · Abstract (English)
As social robots and other artificial agents become more conversationally capable, it is important to understand whether the content and meaning of self-disclosure towards these agents changes depending on the agent's embodiment. In this study, we analysed conversational data from three controlled experiments in which participants self-disclosed to a human, a humanoid social robot, and a disembodied conversational agent. Using sentence embeddings and clustering, we identified themes in participants' disclosures, which were then labelled and explained by a large language model. We subsequently assessed whether these themes and the underlying semantic structure of the disclosures varied by agent embodiment. Our findings reveal strong consistency: thematic distributions did not significantly differ across embodiments, and semantic similarity analyses showed that disclosures were expressed in highly comparable ways. These results suggest that while embodiment may influence human behaviour in human-robot and human-agent interactions, people tend to maintain a consistent thematic focus and semantic structure in their disclosures, whether speaking to humans or artificial interlocutors.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。