Fringe News Networks: Dynamics of US News Viewership following the 2020 Presidential Election

The growing political polarization of the American electorate over the last several decades has been widely studied and documented. During the administration of President Donald Trump, charges of "fake news" made social and news media not only the means but, to an unprecedented extent, the topic of political communication. Using data from before the November 3rd, 2020 US Presidential election, recent work has demonstrated the viability of using YouTube's social media ecosystem to obtain insights into the extent of US political polarization as well as the relationship between this polarization and the nature of the content and commentary provided by different US news networks. With that work as background, this paper looks at the sharp transformation of the relationship between news consumers and here-to-fore "fringe" news media channels in the 64 days between the US presidential election and the violence that took place at US Capitol on January 6th. This paper makes two distinct types of contributions. The first is to introduce a novel methodology to analyze large social media data to study the dynamics of social political news networks and their viewers. The second is to provide insights into what actually happened regarding US political social media channels and their viewerships during this volatile 64 day period.

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

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.