Measuring Stereotypes using Entity-Centric Data
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Stereotypes inform how we present ourselves and others, and in turn how we behave. They are thus important to measure. Recent work has used projections of embeddings from Distributional Semantic Models (DSMs), such as BERT, to perform these measurements. However, DSMs capture cognitive associations that are not necessarily relevant to the interpersonal nature of stereotyping. Here, we propose and evaluate three novel, entity-centric methods for learning stereotypes from Twitter and Wikipedia biographies. Models are trained by leveraging the fact that multiple phrases are applied to the same person, magnifying the person-centric nature of the learned associations. We show that these models outperform existing approaches to stereotype measurement with respect to 1) predicting which identities people apply to themselves and others, and 2) quantifying stereotypes on salient social dimensions (e.g. gender). Via a case study, we also show the utility of these models for future questions in computational social science.
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