  While existing work on studying bias in NLP focues on negative or pejorative
language use, Govindarajan et al. (2023) offer a revised framing of bias in
terms of intergroup social context, and its effects on language behavior. In
this paper, we investigate if two pragmatic features (specificity and affect)
systematically vary in different intergroup contexts -- thus connecting this
new framing of bias to language output. Preliminary analysis finds modest
correlations between specificity and affect of tweets with supervised
intergroup relationship (IGR) labels. Counterfactual probing further reveals
that while neural models finetuned for predicting IGR labels reliably use
affect in classification, the model's usage of specificity is inconclusive.
Code and data can be found at: https://github.com/venkatasg/intergroup-probing

Computation and Language;Computers and Society