# Identifying Partisan Slant in News Articles and Twitter during Political Crises

## TL;DR

This study analyzes how mainstream news and Twitter posts during Ukraine's 2013-2014 crises reveal political slant, using NLP to infer partisanship with high accuracy, enhancing understanding of media influence on public opinion.

## Contribution

Introduces a scalable NLP method to detect political slant in large-scale news and social media data during crises, revealing media dynamics and partisan opinion formation.

## Key findings

- Political slant can be inferred with high accuracy from linguistic markers.
- Mainstream and social media exhibit distinct framing patterns during crises.
- The interplay between media types influences public opinion formation.

## Abstract

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.