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

TL;DR

The paper investigates cross-cultural differences in public affective expression by comparing valence and arousal on US Twitter/X and Chinese Sina Weibo using the NRC-VAD lexicon. It tests six functional forms of valence-arousal relationships and finds an asymmetric V-shaped relationship with a negativity bias and negativity offset across both platforms, with higher arousal in the US. By linking language patterns via 1-2-gram features and 2,000 LDA topics, the study reveals distinct lexical and topical cues between Twitter and Weibo that align with cultural norms in emotional expression. Overall, the findings highlight how language, culture, and platform shape public affect, offering implications for cross-cultural sentiment analysis and digital social science research. $A \\sim \\beta_0 + \\beta_1|V| + \\beta_2I|V| + \\beta_3I|V| + \\epsilon$ represents the best-fitting functional form, capturing the asymmetric V-shaped relationship between arousal $A$ and valence $V$.

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.

Figures (10)

• Figure 1: The analysis pipeline of this paper compares cultural differences in affective expressions using large-scale social media data. We examine the functional relationship between valence and arousal and explore the differences through language analysis methods.
• Figure 2: Scatter plots of valence (x-axis) and arousal (y-axis) of Twitter (blue) and Weibo (red) posts. The lines of best fit for Model 6's function are appended to the plot(Twitter: solid line, Weibo: dashed line). The model is tested with within-person intercept and slope.
• Figure 3: Words and phrases associated with valence and arousal on Twitter and Weibo (translated) from the top 15 phrases for effect strength (Pearson $r$), colored by frequency. Statistically significant ($p < .05$, two-tailed t-test, Benjamini-Hochberg corrected).
• Figure 4: Topics associated with valence and arousal on Twitter, sorted by effect size (Pearson $r$). Each point is a topic, and statistically significant topics ($p < .05$, two-tailed t-test, Benjamini-Hochberg corrected) are shown in dark gray. The X-axis is the Pearson $r$ with valence and the Y-axis with arousal. The top 5 words in each topic are shown.
• Figure 5: Topics associated with valence and arousal on Weibo, sorted by effect size (Pearson $r$). Each point is a topic and statistically significant topics ($p < .05$, two-tailed t-test, Benjamini-Hochberg corrected) are shown in dark gray. The X-axis is the Pearson $r$ with valence and the Y-axis with arousal. English translations of the top 5 words in each topic are shown.