  In this paper, we are interested in understanding the interrelationships
between mainstream and social media in forming public opinion during mass
crises, specifically in regards to how events are framed in the mainstream news
and on social networks and to how the language used in those frames may allow
to infer political slant and partisanship. We study the lingual choices for
political agenda setting in mainstream and social media by analyzing a dataset
of more than 40M tweets and more than 4M news articles from the mass protests
in Ukraine during 2013-2014 - known as "Euromaidan" - and the post-Euromaidan
conflict between Russian, pro-Russian and Ukrainian forces in eastern Ukraine
and Crimea. We design a natural language processing algorithm to analyze at
scale the linguistic markers which point to a particular political leaning in
online media and show that political slant in news articles and Twitter posts
can be inferred with a high level of accuracy. These findings allow us to
better understand the dynamics of partisan opinion formation during mass crises
and the interplay between main- stream and social media in such circumstances.
