Quantifying Intimacy in Language

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.

https://arxiv.org/pdf/2011.03020.pdf

亲密度是我们社交的基础方面。语言通过主题和其他的方式包含社交信息。因此,我们为计算语言中的亲密度设计了一种架构,这个架构包括一个数据集和一个基于深度学习的算法用于预测亲密度等级(人类的预测精度是0.87)。通过对数据集中80.5百万个问题进行分析,我们得知人们通过在语言中运用社交技巧去匹配社交关系中的亲密度。另外,我们还发现人们会对特定的性别,社交距离以及观众指制定亲密度以匹配这些社交标准。我们的工作展示了通过语言获得的亲密度是普遍的切对社交多元性有巨大影响的指标。

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