The landscape of impoliteness research
By Marta Dynel. 2015
103 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Communication, Language and Linguistics
View Paper: https://www.semanticscholar.org/paper/05b6ff6a0ce0714decaceecdd36694043513a733
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yejin Choi
- Noah A. Smith
- Maarten Sap
- Swabha Swayamdipta
- Yulia Tsvetkov
- Mohit Bansal
- Luke S. Zettlemoyer
- Hannaneh Hajishirzi
In Communication
In Language and Linguistics
- Saadia Gabriel
- Xuhui Zhou
- Noah A. Smith
- Yejin Choi
- Dan Jurafsky
- Swabha Swayamdipta
- Maarten Sap
- Jillian Grennan
Citations in the corpus
- Challenges in Automated Debiasing for Toxic Language Detection (2021, background): “…most previous analyses of this bias relied on a simple list of “bad” words (Davidson et al., 2019; Dinan et al., 2019), 4 we take a more nuanced view of how lexical items can convey toxicity, inspired by work in pragmatics and sociolin-guistics of rudeness (Dynel, 2015; Kasper, 1990, inter alia ).”
- Social Bias Frames: Reasoning about Social and Power Implications of Language (2019, background): “Intent to offend captures whether the perceived motivation of the author was to offend, which is key to understanding how it is received (Kasper, 1990; Dynel, 2015).”