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We study the Twitter conversation following the August 2017 `Unite the Right'rally in Charlottesville, Virginia, using tools from network analysis and datascience. We use media followership on Twitter and principal component analysis(PCA) to compute a `Left'/`Right' media score on a one-dimensional axis tocharacterize nodes. We then use these scores, in concert with retweetrelationships, to examine the structure of a retweet network of approximately300,000 accounts that communicated with the #Charlottesville hashtag. Theretweet network is sharply polarized, with an assortativity coefficient of 0.8with respect to the sign of the media PCA score. Community detection using twoapproaches, a Louvain method and InfoMap, yields largely homogeneouscommunities in terms of Left/Right node composition. When comparing tweetcontent, we find that tweets about `Trump' were widespread in both the Left andRight, though the accompanying language was unsurprisingly different. Nodeswith large degrees in communities on the Left include accounts that areassociated with disparate areas, including activism, business, arts andentertainment, media, and politics. Support of Donald Trump was a common threadamong the Right communities, connecting communities with accounts thatreference white-supremacist hate symbols, communities with influentialpersonalities in the alt-right, and the largest Right community (which includesthe Twitter account FoxNews).

Online reactions to the 2017 'Unite the Right' rally in Charlottesville: Measuring polarization in Twitter networks using media followership