Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies
This addresses the problem of detecting subtle government media manipulation for researchers and policymakers, though it is incremental in applying existing political science concepts to NLP.
The study analyzed 13 years of Russian newspaper articles to identify subtle media manipulation strategies, finding that mentions of the U.S. increased after economic downturns in Russia, with a focus on U.S. moral failings and threats.
Amidst growing concern over media manipulation, NLP attention has focused on overt strategies like censorship and "fake news'". Here, we draw on two concepts from the political science literature to explore subtler strategies for government media manipulation: agenda-setting (selecting what topics to cover) and framing (deciding how topics are covered). We analyze 13 years (100K articles) of the Russian newspaper Izvestia and identify a strategy of distraction: articles mention the U.S. more frequently in the month directly following an economic downturn in Russia. We introduce embedding-based methods for cross-lingually projecting English frames to Russian, and discover that these articles emphasize U.S. moral failings and threats to the U.S. Our work offers new ways to identify subtle media manipulation strategies at the intersection of agenda-setting and framing.