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Polarization among US political parties, media and elites is a widely studiedtopic. Prominent lines of prior research across multiple disciplines haveobserved and analyzed growing polarization in social media. In this paper, wepresent a new methodology that offers a fresh perspective on interpretingpolarization through the lens of machine translation. With a novel propositionthat two sub-communities are speaking in two different \emph{languages}, wedemonstrate that modern machine translation methods can provide a simple yetpowerful and interpretable framework to understand the differences between two(or more) large-scale social media discussion data sets at the granularity ofwords. Via a substantial corpus of 86.6 million comments by 6.5 million userson over 200,000 news videos hosted by YouTube channels of four prominent USnews networks, we demonstrate that simple word-level and phrase-leveltranslation pairs can reveal deep insights into the current political divide --what is \emph{black lives matter} to one can be \emph{all lives matter} to theother.

We Don't Speak the Same Language: Interpreting Polarization through Machine Translation