论文标题
用语言量化亲密关系
Quantifying Intimacy in Language
论文作者
论文摘要
亲密关系是我们在社会环境中与他人联系的基本方面。语言通过主题和其他更微妙的线索（例如语言对冲和宣誓就职）来编码亲密关系的社会信息。在这里，我们介绍了一个新的计算框架，以使用随附的数据集和深度学习模型来研究语言中的亲密关系表达式，以准确预测问题的亲密关系水平（Pearson's R = 0.87）。通过分析社交媒体，书籍和电影中的8050万个问题的数据集，我们表明个人采用人际务实的动作以其语言来使他们的亲密关系与社交环境保持一致。然后，在三项研究中，我们进一步证明了个人如何调节自己的亲密关系，以符合性别，社会距离和受众的社会规范，每个人都验证了社会心理学研究的关键发现。我们的工作表明，亲密关系是语言的普遍和有影响力的社会层面。
Intimacy is a fundamental aspect of how we relate to others in social settings. Language encodes the social information of intimacy through both topics and other more subtle cues (such as linguistic hedging and swearing). Here, we introduce a new computational framework for studying expressions of the intimacy in language with an accompanying dataset and deep learning model for accurately predicting the intimacy level of questions (Pearson's r=0.87). Through analyzing a dataset of 80.5M questions across social media, books, and films, we show that individuals employ interpersonal pragmatic moves in their language to align their intimacy with social settings. Then, in three studies, we further demonstrate how individuals modulate their intimacy to match social norms around gender, social distance, and audience, each validating key findings from studies in social psychology. Our work demonstrates that intimacy is a pervasive and impactful social dimension of language.
