用网络模型分析文本中的人、事、对象，揭示阴谋论与心理对话中的情感差异。
The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text
- 将文本解析为人物-事件-目标三元组，构建可解释的认知网络
- 发现高阴谋论文本中代词与动作关联频率是低阴谋论的两倍
- 可对比人类与大模型在心理治疗语境下情绪表达的差异
我们提出目标-事件-代理网络（TEA Nets）作为计算框架，从文本中提取主体（‘代理’）、动词（‘事件’）和客体（‘目标’）。该框架基于认知网络科学与人工智能，以开源Python库实现。我们在三个案例研究中验证其有效性：在LOCO阴谋论语料库中，高阴谋论文本（4,227篇）将代词（‘我’、‘你’、‘我们’）与相同动作连接的频率是低相似性文本的两倍；高阴谋论文本中，以‘你’、‘人们’等人物为中心的元素通过引发愤怒的动作相连，显著高于随机基线（z = 2.63, p < .05），而低相似性文本更强调科学角色（如‘研究员’、‘科学家’）。在212篇人类与200篇大模型生成的心理治疗语料（HOPE与CounseLLMe数据集）中，Claude 3 Haiku、GPT-3.5和人类表达悲伤词频高于随机预期，但Haiku的悲伤强度低于人类（U = 1243.5, p = .036）。结果表明，TEA Nets能有效提取叙事中的情绪、句法与语义信息，为结合认知网络科学开展文本分析开辟新路径。
原文摘要 · Abstract (English)
We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (``Agents"), verbs (``Events"), and objects (``Targets") from texts. Grounded in cognitive network science and artificial intelligence, TEA Nets are implemented as an open-source Python library. We test TEA Nets in three case studies, demonstrating the framework's ability to perform interpretable emotion detection, semantic frame analyses, and linguistic inquiries across conspiracy texts and textual responses generated by LLMs. In the LOCO conspiracy corpus, TEA Nets revealed that highly conspiratorial narratives (4,227 texts) linked personal pronouns (``I", ``you", ``we") with the same actions twice as frequently as low-similarity conspiracy narratives. High-conspiracy narratives connected person-focused elements (``you", ``people") through actions eliciting anger above the random baseline ($z = 2.63, p < .05$), a trend absent in low-similarity conspiracy narratives, which emphasized scientific actors (``researcher", ``scientist"). In the HOPE and CounseLLMe datasets of 212 (human) and 200 (LLM-based) psychotherapy transcripts, respectively, TEA Nets highlighted emotional differences. When expressing feelings, Claude 3 Haiku, GPT-3.5, and humans used sad words with higher frequency than random expectations but Haiku expressed sadness with lower emotional intensity than humans ($U = 1243.5, p = .036$). We discuss these differences in the context of psychotherapy training on LLM-simulated patients. Our results show that Target-Event-Agent Networks can extract relevant emotional, syntactic, and semantic insights from narratives, opening new avenues for text analysis with cognitive network science.
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