Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies

TL;DR

This study uses computational methods to analyze subtle Russian media manipulation strategies, focusing on agenda-setting and framing over 13 years of news articles, revealing patterns of distraction and moral framing related to U.S. topics.

Contribution

It introduces embedding-based cross-lingual framing analysis and uncovers new insights into subtle government media manipulation tactics in Russia.

Findings

Articles mention the U.S. more after Russian economic downturns

Russian articles emphasize U.S. moral failings and threats

Distraction strategy correlates with economic events

Abstract

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.