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A computational approach to politeness with application to social factors

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" does not allow redistributing the text itself. Original — aclanthology.org/P13-1025.pdf

Summary

The paper addresses how to detect politeness in text automatically. It focuses on requests, because asking something of another person is an imposition and tends to trigger politeness strategies. The authors collected requests from Wikipedia user talk-pages and Stack Exchange comments. They had a subset rated on Amazon Mechanical Turk, producing what they describe as the largest publicly released politeness-annotated corpus at the time. Each two-sentence request received five slider ratings, which were normalized per worker using z-scores and then averaged.

The data broadly support Brown and Levinson's theory but also reveal context effects. For example, "please" reads as polite mid-sentence but impolite at the start. Guided by these findings, the authors built a classifier with domain-independent lexical, sentiment and dependency features, such as indirectness, hedging, gratitude, deference and modality. It approaches human agreement and transfers across domains.

Applied to social questions, the framework shows that politeness is inversely related to power. This illustrates how pragmatic signals can be measured at scale.

Key points

  • Data size: The raw data contain 35,661 Wikipedia and 373,519 Stack Exchange requests. Of these, 4,353 and 6,604 were annotated (over 10,000 in total) by 219 and 212 annotators respectively, with five ratings per request.
  • Score distribution and agreement: Normalized scores average 0 with a standard deviation of about 0.7. Annotators agree more strongly at the extremes: all five raters made the same binary judgment for 62%/37% (Wiki/SE) of bottom-quartile requests and 51%/46% of top-quartile requests, but for only 3–8% of requests in the middle quartiles.
  • Linguistic findings:
    • Counterfactual "would"/"could" are rated more polite than "can"/"will".
    • Sentence-initial "you" and sentence-initial "please" signal impoliteness.
    • Hedges and first-person plural forms raise perceived politeness.
  • Power findings:
    • Polite Wikipedia editors are more likely to win admin elections but become less polite once elected.
    • Top-reputation Stack Exchange users are less polite than low-reputation users.
    • These results were confirmed on human-labeled data alone.
  • Preliminary classifier-based observations:
    • Female Wikipedians are generally more polite.
    • Wikipedians from the U.S. Midwest are the most polite of the census-defined regions.
    • Programming-language communities on Stack Exchange differ significantly in politeness.
    • The corpus is publicly available via MPI-SWS.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

139 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 129 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{danescuniculescumizil2013computational,
  title = {{A computational approach to politeness with application to social factors}},
  author = {Danescu-Niculescu-Mizil, Cristian and Sudhof, Moritz and Jurafsky, Dan and Leskovec, Jure and Potts, Christopher},
  booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2013},
  url = {https://aclanthology.org/P13-1025/}
}

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