# Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination

arXiv: https://arxiv.org/abs/2401.05254 · HTML: https://mlsanity.com/paper/2401.05254/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 83 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text](https://mlsanity.com/paper/2005.11882/) (2020)
- [Understanding and Measuring Psychological Stress using Social Media](https://mlsanity.com/paper/1811.07430/) (2018)
- [What Twitter Profile and Posted Images Reveal About Depression and Anxiety](https://mlsanity.com/paper/1904.02670/) (2019)
- [Studying Cultural Differences in Emoji Usage across the East and the West](https://mlsanity.com/paper/1904.02671/) (2019)

## Built on by (most cited first, 1 of 1)

- [Unveiling Affective Polarization Trends in Parliamentary Proceedings](https://mlsanity.com/paper/2512.05231/) (2025)

## Abstract (arXiv)

While affective expressions on social media have been extensively studied, most research has focused on the Western context. This paper explores cultural differences in affective expressions by comparing valence and arousal on Twitter/X (geolocated to the US) and Sina Weibo (in Mainland China). Using the NRC-VAD lexicon to measure valence and arousal, we identify distinct patterns of emotional expression across both platforms. Our analysis reveals a functional representation between valence and arousal, showing a negative offset in contrast to traditional lab-based findings which suggest a positive offset. Furthermore, we uncover significant cross-cultural differences in arousal, with US users displaying higher emotional intensity than Chinese users, regardless of the valence of the content. Finally, we conduct a comprehensive language analysis correlating n-grams and LDA topics with affective dimensions to deepen our understanding of how language and culture shape emotional expression. These findings contribute to a more nuanced understanding of affective communication across cultural and linguistic contexts on social media.