# Studying Politeness across Cultures Using English Twitter and Mandarin Weibo

arXiv: https://arxiv.org/abs/2008.02449 · HTML: https://mlsanity.com/paper/2008.02449/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 10 of 10)

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- [Computational Politeness in Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.12814/) (2024)
- [How Did We Get Here? Summarizing Conversation Dynamics](https://mlsanity.com/paper/2404.19007/) (2024)
- [Social Media Reveals Urban-Rural Differences in Stress across China](https://mlsanity.com/paper/2110.15726/) (2021)
- [The FIX Benchmark: Extracting Features Interpretable to eXperts](https://mlsanity.com/paper/2409.13684/) (2024)
- [Comparing Styles across Languages: A Cross-Cultural Exploration of Politeness](https://mlsanity.com/paper/2310.07135/) (2023)
- [Understanding the Impact of Culture in Assessing Helpfulness of Online Reviews](https://mlsanity.com/paper/2305.04836/) (2023)
- [Historical patterns of rice farming explain modern-day language use in China and Japan more than modernization and urbanization](https://mlsanity.com/paper/2308.15352/) (2023)
- [Splits! Flexible Sociocultural Linguistic Investigation at Scale](https://mlsanity.com/paper/2504.04640/) (2025)
- [REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality](https://mlsanity.com/paper/2610.13797/) (2026)

## Abstract (arXiv)

Modeling politeness across cultures helps to improve intercultural communication by uncovering what is considered appropriate and polite. We study the linguistic features associated with politeness across US English and Mandarin Chinese. First, we annotate 5,300 Twitter posts from the US and 5,300 Sina Weibo posts from China for politeness scores. Next, we develop an English and Chinese politeness feature set, `PoliteLex'. Combining it with validated psycholinguistic dictionaries, we then study the correlations between linguistic features and perceived politeness across cultures. We find that on Mandarin Weibo, future-focusing conversations, identifying with a group affiliation, and gratitude are considered to be more polite than on English Twitter. Death-related taboo topics, lack of or poor choice of pronouns, and informal language are associated with higher impoliteness on Mandarin Weibo compared to English Twitter. Finally, we build language-based machine learning models to predict politeness with an F1 score of 0.886 on Mandarin Weibo and a 0.774 on English Twitter.