# Quantifying Intimacy in Language

arXiv: https://arxiv.org/abs/2011.03020 · HTML: https://mlsanity.com/paper/2011.03020/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 25 of 25)

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- [UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction](https://mlsanity.com/paper/2303.01194/) (2023)
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- [Analyzing the Intensity of Complaints on Social Media](https://mlsanity.com/paper/2204.09366/) (2022)
- [Sensemaking About Contraceptive Methods Across Online Platforms](https://mlsanity.com/paper/2301.09295/) (2023)
- [HULAT at SemEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy Analysis](https://mlsanity.com/paper/2302.12794/) (2023)
- [WADER at SemEval-2023 Task 9: A Weak-labelling framework for Data augmentation in tExt Regression Tasks](https://mlsanity.com/paper/2303.02758/) (2023)
- [tmn at SemEval-2023 Task 9: Multilingual Tweet Intimacy Detection using XLM-T, Google Translate, and Ensemble Learning](https://mlsanity.com/paper/2304.04054/) (2023)
- [OPI at SemEval 2023 Task 9: A Simple But Effective Approach to Multilingual Tweet Intimacy Analysis](https://mlsanity.com/paper/2304.07130/) (2023)
- [Text Intimacy Analysis using Ensembles of Multilingual Transformers](https://mlsanity.com/paper/2312.02590/) (2023)

## Abstract (arXiv)

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.