研究聊天机器人语言性格如何影响用户决策与感知
The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisions
- 设计八种不同语言性格的聊天机器人，测试态度、权威、推理三维度影响
- 悲观型机器人虽降低用户好感与信任感，却反而促使更多捐款
- 信任、能力、共情感知是决定捐赠的关键心理因素
大型语言模型驱动的对话代理（CAs）正越来越能通过语言展现复杂个性，但其对用户的影响尚不明确。本研究在众包实验中让360名参与者与8个分别体现三种语言特征组合（态度：乐观/悲观；权威：权威/顺从；推理：情感/理性）的聊天机器人互动，考察其在慈善捐赠情境中的作用。结果显示，尽管机器人整体个性未显著影响捐赠决策，却显著改变了用户的感知与情绪反应。与悲观型机器人互动的用户情绪状态更低，对公益事业的认同感更弱，认为机器人更不可信、能力更差，但反而更倾向于捐款。信任度、胜任力和情境共情感显著预测了捐赠行为。研究揭示了对话代理作为潜在操纵工具的风险，可能在无形中影响用户认知与选择。
原文摘要 · Abstract (English)
Large Language Model-powered conversational agents (CAs) are increasingly capable of projecting sophisticated personalities through language, but how these projections affect users is unclear. We thus examine how CA personalities expressed linguistically affect user decisions and perceptions in the context of charitable giving. In a crowdsourced study, 360 participants interacted with one of eight CAs, each projecting a personality composed of three linguistic aspects: attitude (optimistic/pessimistic), authority (authoritative/submissive), and reasoning (emotional/rational). While the CA's composite personality did not affect participants' decisions, it did affect their perceptions and emotional responses. Particularly, participants interacting with pessimistic CAs felt lower emotional state and lower affinity towards the cause, perceived the CA as less trustworthy and less competent, and yet tended to donate more toward the charity. Perceptions of trust, competence, and situational empathy significantly predicted donation decisions. Our findings emphasize the risks CAs pose as instruments of manipulation, subtly influencing user perceptions and decisions.
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