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

2024-01-10

arXiv.org

AI Summary

This study investigates cultural differences in emotional expression between U.S. and Chinese social media users, focusing on valence and arousal distributions on Twitter/X (U.S.) versus Weibo (China). Methodologically, it quantifies affective dimensions using the NRC-VAD lexicon, complemented by n-gram analysis and LDA topic modeling for cross-platform comparison. Results reveal, for the first time in authentic social media contexts, a significant negative correlation between valence and arousal—contrary to the positive correlation typically observed in controlled laboratory settings. Moreover, U.S. users exhibit significantly higher average arousal than Chinese users, an effect independent of sentiment polarity. The study proposes a culture-sensitive functional model of emotional expression, grounded in empirical cross-platform evidence. This model advances cross-lingual affective computing by providing both a theoretically informed framework and a reproducible empirical foundation for future research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

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.

Problem

• Examines cultural differences in emotional expressions
• Compares valence and arousal on Twitter and Weibo
• Identifies distinct emotional patterns across platforms

Innovation

• NRC-VAD lexicon for emotion analysis
• Cross-cultural Twitter/X and Sina Weibo comparison
• N-grams and LDA for language analysis