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

Paper · 2018 · Anjalie Field, Doron Kliger, Shuly Wintner et al.

Summary

Most NLP work on state media manipulation targets fabricated stories and censorship. This paper looks at quieter tactics, using two political-science concepts: agenda-setting (which topics get covered) and framing (which aspects of a topic are emphasized). The authors study more than 100K articles from the Russian newspaper Izvestia (2003–2016). They compare the share of U.S.-related coverage with Russian economic indicators, the RTSI stock index and GDP, using Pearson correlation and Granger causality. The analysis shows that a falling stock market is followed within about a month by more attention to the U.S. To explain how that coverage is slanted, they transfer English framing categories from the Policy Frames Codebook and Media Frames Corpus (MFC) to Russian. The method is semi-supervised and builds corpus-specific frame lexicons from word embeddings. The projected frames show that this coverage stresses American moral failings and dangers facing the U.S. The work offers a scalable, theory-based way to detect distraction strategies, especially in languages without framing annotations.

Key points

  • Data and coverage metrics. The corpus is Izvestia from 2003–2016 (100K+ articles). U.S. coverage is measured two ways:
    • article level: the share of articles mentioning the U.S. at least twice;
    • word level: the normalized frequency of U.S. mentions.
  • Correlations. Coverage is negatively correlated with the economy at every time resolution:
    • yearly GDP: r = -0.83 (article level) and -0.79 (word level);
    • quarterly GDP: -0.69 and -0.65;
    • monthly RTSI: -0.54 and -0.52.
    • 2008 is an outlier, explained by the U.S. financial crisis and that year's U.S. election.
  • Granger causality.
    • The 1-month-lagged percent change in RTSI significantly predicts U.S. coverage (p ≈ 0.03), and the coefficient is negative.
    • The 2-month lag is not significant, nor are quarterly or yearly models.
    • The reverse direction (coverage predicting the market) shows no significant effect.
  • Frame projection. English frames from the 15-category Policy Frames Codebook (MFC) are projected onto the Russian corpus through embedding-based lexicons. This removes the need for Russian training labels.
  • Distraction strategies found. Close reading of post-downturn articles revealed three patterns:
    • portraying the U.S. as villainous, using Morality and Legality frames;
    • highlighting threats inside the U.S., such as terrorism;
    • downplaying U.S. military strength relative to Russia's.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Stanford UniversityStanford · United States310 works · 284,480 citations🎓Carnegie Mellon UniversityPittsburgh · United States365 works · 158,048 citations🎓University of HaifaHaifa · Israel27 works · 871 citations

📚 Shelves 3 Social Sciences · 744 works Sociology and Political Science · 221 works Communication · 44 works

Cite

Cite as 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. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/d18-1393

@inproceedings{field2018framing,
  title = {{Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies}},
  author = {Anjalie Field, Doron Kliger, Shuly Wintner, Jennifer Pan, Dan Jurafsky and Yulia Tsvetkov},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1393},
  url = {https://doi.org/10.18653/v1/d18-1393}
}

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