  We provide an NLP framework to uncover four linguistic dimensions of
political polarization in social media: topic choice, framing, affect and
illocutionary force. We quantify these aspects with existing lexical methods,
and propose clustering of tweet embeddings as a means to identify salient
topics for analysis across events; human evaluations show that our approach
generates more cohesive topics than traditional LDA-based models. We apply our
methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the
discussion of these events is highly polarized politically and that this
polarization is primarily driven by partisan differences in framing rather than
topic choice. We identify framing devices, such as grounding and the
contrasting use of the terms "terrorist" and "crazy", that contribute to
polarization. Results pertaining to topic choice, affect and illocutionary
force suggest that Republicans focus more on the shooter and event-specific
facts (news) while Democrats focus more on the victims and call for policy
changes. Our work contributes to a deeper understanding of the way group
divisions manifest in language and to computational methods for studying them.
