Twitter and political culture: Short text embeddings as a window into political fragmentation

Mapping polarization and relationships in political discourse on social media is challenging since politicians' positions and relationships can be hard to pin down. In this paper, we attempt to use politicians' tweets as a metric of their affinities using representation learning, by modifying the Word2Vec method such that politicians are directly encoded into a Euclidean space. Our analysis of Indian politicians shows that the relatively populous, linguistically more homogeneous northern states are cohesively clustered based on their party affiliations, whereas southern states cluster based on geography. We propose that computational methods can be useful in examining the tensions of regionalist tendencies against dominant national political narratives.