Paper 1: Danescu-Niculescu-Mizil, Cristian, et al. “A computational approach to politeness with application to social factors.” arXiv preprint arXiv:1306.6078 (2013).
This papers analyzes Wikipedia and StackExchange requests data for politeness markers based on politeness theory and builds a predictive model to judge politeness index of a conversation. There are several strong points about this paper such as data filtering, balanced analysis, and feature justification. I specifically liked the part where strong and weak politeness annotations based on percentile agreements. I also like the application of politeness to power and status.
Even though the paper claims to be domain independent, what is considered to be polite can change across communities. Very obvious and extreme examples of this can be 4chan and Wikipedia. StackExchange and Wikipedia are both information-driven communities in which conversations are situated on a much different level than other social networks such are Twitter and Facebook. Further, politeness can also change based on the context of the social groups it is analyzed in. For example, what is considered as polite in a group of strangers might differ from politeness norms in a group of friends or colleagues.
Few questions come up after reading the annotation procedure of the paper.
- Is it realistic to get such perfect 50-50 distribution of politeness scores in both datasets? How both datasets have almost an equal number of polite and impolite requests? (It might be a coincidence, just looks a bit weird!)
- The median and variance can depend on how many sample points are chosen. It’s not clear how many randomly scored annotations were used for comparison in Wilcoxon signed rank test.
- Further, the median of both randomized scores is the same. (Again, might be the coincidence but looks weird! )
Further, the author’s findings that failed candidates get more polite after elections are inconsistent with several works analyzing the effect of the community feedback on user behavior. Considering failure in the election as a negative feedback, users should be expected to get more hostile/deviant. This raises a question: Does the correlation of politeness with power change over incentive driven and punishment driven communities?
Using conversation analysis principles in politeness measure: Even though the turker study was conducted with the context of request and answer pair, the linguistic measures were performed only on the request texts. Politeness is conversation driven. It can change over a series of message exchanges based on how the conversation flows. Principles of conversation analysis can be applied here to get a deeper understanding of politeness in conversation rather than just a part of a text. Specifically, talk-in-turns, attempts to repair problems and action formation aspects of conversation can be important in identifying politeness.
Lastly, the work done in this paper can be used to construct the model for assessing the general concept of “status” on social media. This work proves that politeness and status are correlated. Other aspects of status such as respect, authority, credibility, decisiveness can be assimilated to form a social status model. Such a model, as described in earlier class sessions can nudge people to be more source aware, civil, polite and respectful based on the status index in that community.
