  Identity is one of the most commonly studied constructs in social science.
However, despite extensive theoretical work on identity, there remains a need
for additional empirical data to validate and refine existing theories. This
paper introduces a novel approach to studying identity by enhancing word
embeddings with socio-demographic information. As a proof of concept, we
demonstrate that our approach successfully reproduces and extends established
findings regarding gendered self-views. Our methodology can be applied in a
wide variety of settings, allowing researchers to tap into a vast pool of
naturally occurring data, such as social media posts. Unlike similar methods
already introduced in computer science, our approach allows for the study of
differences between social groups. This could be particularly appealing to
social scientists and may encourage the faster adoption of computational
methods in the field.
