Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013) “A computational approach to politeness with application to social factors”.
This paper focus on linguistic aspects of politeness on social platforms like Wikipedia, Stack Exchange. The author conducted linguistic analysis for politeness using two classifiers, bag of words classifier and linguistically informed classifier. They were able to report algorithm results close to human performance. Also, the analysis of the relationship between politeness levels and social power shows a negative correlation between politeness and power on Stack Exchange or Wikipedia.
This paper is good to me. The illustration of the use of common phrases (strategies) relative to politeness is the way of how we normally make this judgement only from verbal words intuitively. However, bag of word may not entirely reflect the politeness. Maybe using bag of phrases, and analysis the grammatical structure of the sentences. Because the use of formal and complete grammar may indicate politeness. Also what might be a drawback is that, when building two classifiers to predict politeness, the author tested the classifiers both in-domain (training and testing data from the same source) and cross-domain (training on Wikipedia and testing on Stack Exchange, and vice versa). To me these are communities with distinctive characteristics, Wikipedia is more formal, and the use of word is more precise, while stack exchange is more casual in terms of use of language. A short yet precise answer in stack exchange would be viewed by human as polite, but not by the classifier. The domain transfer here is worthy some discussion. Or what about combining these two data sources and perform a Leave-one-out cross-validation (LOOCV) ?
Voigt, R., Camp, N. P., Prabhakaran, V., Hamilton, W. L., Hetey, R. C., Griffiths, C. M., … & Eberhardt, J. L. (2017). Language from police body camera footage shows racial disparities in officer respect. Proceedings of the National Academy of Sciences, 201702413.
This paper tries to study the respect-level between different races, gender and nationality etc. using transcribed data from Oakland Police Department body camera videos. They extracted the respectfulness of police officer language by applying computational linguistic methods on transcripts. Disparities are shown in the speaking toward black and white community members. The first thing I can think about is the prior of such events. In the case of traffic stops, whether the target comply with officer’s instructions, whether the car is clean. These are small cues that will affect officer’s actions. I’m sure they won’t be so polite after a highway chase. So, audio transcription alone is insufficient to prove this correlation.
