Studying Politeness across Cultures using English Twitter and Mandarin Weibo

Abstract

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 American English and Mandarin Chinese. First, we annotate 5,300 Twitter posts from the United States (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 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 more polite compared to English Twitter. Death-related taboo topics, use of pronouns (with the exception of honorifics), and informal language are associated with higher impoliteness on Mandarin Weibo than on English Twitter. Finally, we build language-based machine learning models to predict politeness with an F1 score of 0.886 on Mandarin Weibo and 0.774 on English Twitter.