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

Anjalie Field, Doron Kliger, Shuly Wintner, Jennifer Pan, Dan Jurafsky, Yulia Tsvetkov

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 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.
Anthology ID:
D18-1393
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3570–3580
Language:
URL:
https://aclanthology.org/D18-1393
DOI:
10.18653/v1/D18-1393
Bibkey:
Cite (ACL):
Anjalie Field, Doron Kliger, Shuly Wintner, Jennifer Pan, Dan Jurafsky, and Yulia Tsvetkov. 2018. Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3570–3580, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies (Field et al., EMNLP 2018)
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