- Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013) “A computational approach to politeness with application to social factors”.
- 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”.
Danescu et al., have proposed a computational approach to politeness with application to social factors. They build a computational framework to study the relationship between politeness and social power, showing how people behave once elevated. The authors have built a new corpus where data comes from 2 online communities – Wikipedia and Stack exchange. To label the data they used Amazon Mechanical Turks and labeled over 10,000 utterances. The Wikipedia data was used to train the politeness classifier whereas Stack Exchange data was used to test the classifier. Authors have constructed a politeness classifier with a wide range of domain-independent lexical, sentiment, and dependency features and presents a comparison between two classifiers – a Bag of Words classifier and linguistically informed classifier. The classifiers were evaluated in both in-domain and cross-domain settings. Looking at the results of cross-domain setting, I wonder if the politeness classifier will give same or better results for a different corpus from a different domain. Their results confirm shows a significant relationship between politeness and social power, showing that polite Wikipedia editors, once elevated, becomes less polite and Stack Exchange users at the top of the reputation scale are less polite than those at the bottom. However, it would be interesting to identify a common feature list, irrespective of the domain, given any corpus which can classify polite and impolite requests or posts or replies.
Voigt et al., have also proposed computational linguistic methods to extract the level of respect and politeness automatically from transcripts. The authors of this paper talk about racial disparity between black and white communities by traffic signal officers. The data was collected from the transcribed body camera footage from vehicle stops of white and black community conducted by the Oakland Police Department during April 2014. Since the officers were made to wear the cameras and record their own footage, will they still show racial disparity? Can there be other factors behind it? I really like the approach and the 3 studies – Perceptions of Officer Treatment from Language, Linguistic Correlates of Respect and Racial Disparities in Respect, conducted by the authors. However, I wonder if the results will be the same if a similar study is conducted in different cities (which reports low or high racial disparities.)
