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

TL;DR

This paper introduces a novel approach using machine translation to interpret political polarization on social media by treating different communities as speaking different languages, revealing nuanced differences in discourse.

Contribution

The paper proposes a new methodology applying machine translation to social media data to interpret polarization at the word and phrase level, offering a fresh, interpretable perspective.

Findings

Translation pairs reveal ideological differences like 'Black Lives Matter' vs. 'All Lives Matter'

Method uncovers deep insights into political divides

Large-scale social media data demonstrates the approach's effectiveness

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.