Mapping polarization and relationships in political discourse on
\nsocial media is challenging since politicians\u2019 positions and relationships
\ncan be hard to pin down. In this paper, we attempt to use
\npoliticians\u2019 tweets as a metric of their affinities using representation
\nlearning, by modifying the Word2Vec method such that politicians
\nare directly encoded into a Euclidean space. Our analysis of Indian
\npoliticians shows that the relatively populous, linguistically more
\nhomogeneous northern states are cohesively clustered based on their
\nparty affiliations, whereas southern states cluster based on geography.
\nWe propose that computational methods can be useful in examining
\nthe tensions of regionalist tendencies against dominant national
\npolitical narratives.<\/p>\n","protected":false},"excerpt":{"rendered":"
Mapping polarization and relationships in political discourse on social media is challenging since politicians\u2019 positions and relationships can be hard to pin down. In this paper, we attempt to use politicians\u2019 tweets as a metric of their affinities using representation learning, by modifying the Word2Vec method such that politicians are directly encoded into a Euclidean 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