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• W3091959351 abstract "Polarization among US political parties, media and elites is a widely studied topic. Prominent lines of prior research across multiple disciplines have observed and analyzed growing polarization in social media. In this paper, we present a new methodology that offers a fresh perspective on interpreting polarization through the lens of machine translation. With a novel proposition that two sub-communities are speaking in two different emph{languages}, we demonstrate that modern machine translation methods can provide a simple yet powerful and interpretable framework to understand the differences between two (or more) large-scale social media discussion data sets at the granularity of words. Via a substantial corpus of 86.6 million comments by 6.5 million users on over 200,000 news videos hosted by YouTube channels of four prominent US news networks, we demonstrate that simple word-level and phrase-level translation 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 the other." @default.
• W3091959351 created "2020-10-15" @default.
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• W3091959351 date "2020-10-05" @default.
• W3091959351 modified "2023-09-23" @default.
• W3091959351 title "We Don't Speak the Same Language: Interpreting Polarization through Machine Translation" @default.
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