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

Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias