Verbal harassment is a growing source of psychological stress for people around the world. It occurs both online and offline and relies on language to demean, threaten, or discredit its targets. Unlike other stressors such as loss or uncertainty, verbal harassment aims at silencing its targets by eroding their sense of being heard and weakening their perceived ability to respond. Many individuals lack access to adequate and timely support, however, when they experience such harassment. People increasingly turn to conversational artificial intelligence (AI) such as ChatGPT or dedicated AI companions for emotional support, raising questions about whether it can facilitate the same psychological benefits as actual human empathy. We focus on online contexts as a prevalent application of verbal harassment. We develop and test a psychological framework identifying three key linguistic signals of empathic listening (perspective-taking, emotional validation, and action orientation), that together restore a sense of feeling heard and enhance coping in the context of verbal harassment. We find that LLMs consistently produce language exhibiting stronger empathic-listening markers than human non-experts and trained mental health professionals, promoting more approach-oriented (vs. avoidance-oriented) coping strategies. A subsequent behavioral study shows that these linguistic signals boost recipients' sense of feeling heard and increase their coping self-efficacy. These findings reveal how specific linguistic features create empathic connections between humans and advanced conversational AI and can enhance people's psychological resilience. Our results highlight the potential for AI to serve as a scalable source of emotional support, especially when human support is unavailable or insufficient.

翻译：言语骚扰正日益成为全球人群心理压力的来源。它同时存在于线上线下，通过语言贬低、威胁或诋毁受害者。与损失或不确定性等其他压力源不同，言语骚扰旨在通过削弱受害者被倾听感、降低其感知到的回应能力来使其沉默。然而，许多人在遭遇此类骚扰时缺乏充分及时的支持。人们越来越多地转向ChatGPT等对话式人工智能或专用AI伴侣寻求情感支持，这引发了一个问题：AI是否能产生与真实人类共情相同的心理效益？我们聚焦于言语骚扰的常见应用场景——线上环境。我们开发并验证了一个心理学框架，识别出共情倾听的三个关键语言信号（观点采择、情感验证与行动导向），这些信号共同恢复被倾听感，并增强应对言语骚扰的能力。研究发现，大语言模型生成的语言在共情倾听标记上始终强于人类非专业人士及受训心理健康从业者，能促进更多趋近导向（而非回避导向）的应对策略。后续行为实验表明，这些语言信号能提升接收者的被倾听感并增强其应对自我效能。这些发现揭示了特定语言特征如何在人类与先进对话式AI之间建立共情联结，从而增强人们的心理韧性。我们的结果凸显了AI作为可扩展情感支持来源的潜力——尤其当人类支持不可得或不足时。