A Computational Approach to Politeness with Application to Social Factors
This work addresses the challenge of modeling politeness in language for applications in social computing, though it is incremental as it builds on existing politeness theory with new data and analysis.
The authors tackled the problem of computationally identifying linguistic politeness by developing a classifier based on a new annotated corpus, achieving close to human performance and applying it to show that polite Wikipedia editors are more likely to gain high status but become less polite after elevation, with similar trends on Stack Exchange.
We propose a computational framework for identifying linguistic aspects of politeness. Our starting point is a new corpus of requests annotated for politeness, which we use to evaluate aspects of politeness theory and to uncover new interactions between politeness markers and context. These findings guide our construction of a classifier with domain-independent lexical and syntactic features operationalizing key components of politeness theory, such as indirection, deference, impersonalization and modality. Our classifier achieves close to human performance and is effective across domains. We use our framework to study the relationship between politeness and social power, showing that polite Wikipedia editors are more likely to achieve high status through elections, but, once elevated, they become less polite. We see a similar negative correlation between politeness and power on Stack Exchange, where users at the top of the reputation scale are less polite than those at the bottom. Finally, we apply our classifier to a preliminary analysis of politeness variation by gender and community.