  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.
