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City representation in Soviet propaganda and geographical biases in cultural data

Nature Cities volume 3, pages 146–154 (2026)Cite this article

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Abstract

Cultural representations typically contain illuminating biases. For example, geographical locations are unequally portrayed in media, creating a distorted representation of the world. Identifying and measuring such biases is crucial to understanding both the data and the socio-cultural processes behind them. Here we measured geographical biases in the representation of cities in a large Soviet news-media corpus, highlighting relative emphasis. Combining urban science, cultural geography and digital humanities, we first obtained robust quantitative estimates of representational biases and then qualitatively interpreted the classifications, feeding these interpretations back in an iterative feedback loop. Applied to a corpus of Soviet newsreels, ‘Novosti Dnya’ (News of the Day, short news films shown just before feature films), we find that city representation grows superlinearly with city size and is further biased by city specialization and geographical location. For example, cities with major hydroelectricity and steelworks were overrepresented, while those in the industrial heartland and in non-European socialist countries were underrepresented.

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Fig. 1: Cities of interest on the map of the USSR.
Fig. 2: Observed city mentions vs expectation from population-only and full models.
Fig. 3: Overview of models explaining Soviet city representation.

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Data availability

All data used for the analysis, results of each model for every city of interest and model comparison spreadsheets are available via Github at https://github.com/thummm/soviet_newsreels/.

Code availability

Optimization of all the models described in the text has been done using custom-made code in Wolfram Mathematica 13.1. The corresponding Mathematica worksheets and optimization logs are available via Github at https://github.com/thummm/soviet_newsreels/. Further correspondence and requests should be addressed to the corresponding author.

Change history

  • 26 February 2026

    In the version of this article initially published, a primary affiliation for Mila Oiva was misrepresented as a present address. The corrected present address is FAU, Erlangen-Nuremberg, Germany. The affiliations have been updated in the HTML and PDF versions of the article.

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Acknowledgements

This project was funded by the European Research Area (ERA) Chair project for Cultural Data Analytics (CUDAN), funded through the European Union’s Horizon 2020 research and innovation program (grant number 810961). Additionally, M.V.T. acknowledges support from the Estonian Research Council (ETAG), grant PRG 1059, M.O. acknowledges support from the Horizon Europe Twinning Program, project 101159659, M.S. acknowledges support from the Tallinn University Research Fund project 'Cultural Data Analytics Open Lab 2024–2027'.

Author information

Author notes

  1. Mila Oiva

    Present address: FAU, Erlangen-Nuremberg, Germany

Authors and Affiliations

  1. School of Digital Technologies, Tallinn University, Tallinn, Estonia

    Mikhail V. Tamm

  2. School of Humanities, Tallinn University, Tallinn, Estonia

    Mila Oiva & Mark Mets

  3. Faculty of Humanities, University of Turku, Turku, Finland

    Mila Oiva

  4. Baltic Film, Media and Arts School, Tallinn University, Tallinn, Estonia

    Ksenia D. Mukhina & Maximilian Schich

Authors
  1. Mikhail V. Tamm
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  2. Mila Oiva
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  3. Ksenia D. Mukhina
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  4. Mark Mets
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  5. Maximilian Schich
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Contributions

Conceptualization: M.V.T., M.O., K.D.M., M.M. and M.S. Methodology: M.V.T. Data preparation and cleaning: M.V.T., M.O. and K.D.M. Data analysis: M.V.T. Visualization: M.V.T., K.D.M. and M.S. Writing–original draft: M.V.T. and M.O. Writing–review and editing: M.V.T., M.O., K.D.M., M.M. and M.S.

Corresponding author

Correspondence to Mikhail V. Tamm.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cities thanks Anastasiia Bonch-Osmolovskaia, Vinicius M. Netto and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The workflow pipeline.

The workflow pipeline of the suggested procedure to extract information on media representation of cities. The black arrows correspond to the flow of data. The green arrow denotes classification of hypothetical parameters into relevant and irrelevant according to a predetermined information-theoretic criterion. The red arrow signifies the feedback loop, that is the systematic refinement of the hypothesis based on the qualitative study of model outliers. Icons and diagram templates from igraph under a Creative Commons license CC BY 4.0.

Extended Data Fig. 2 Time evolution of city representation in the population model.

Scatter plots of the number of mentions vs population for the cities in the USSR for 3 periods of equal length: (A) mentions in 1954-64 vs population as of 1959 census; (B) mentions in 1966-76 vs population as of 1970 census; (C) mentions in 1977-87 vs population as of 1979 census. Red lines are best power-law fits with characteristics summarized in Table S3, dashed and dotted lines correspond to confidence intervals with \(p=0.05\) and \(p=0.001\), respectively. Cities with zero mentions (black dots) are shown out of scale. Selected cities are outlined, see discussion in the text.

Extended Data Fig. 3 Snapshots from two News of the Day newsreels.

Snapshots from two exemplary News of the Day newsreel, issues 24, 1954 (top two rows) and 30, 1970 (bottom two rows), with one snapshot per story. Snapshots are accompanied with English translations of the corresponding outlines, mentions of the cities are given in bold. Images from net-film (https://www.net-film.ru/).

Supplementary information

Supplementary Information (download PDF )

Main text of the supplementary materials, including Supplementary Fig. 1 and 8 in-text Tables.

Reporting Summary (download PDF )

Supplementary Table 1 (download XLSX )

Input data and results of all models for all cities of interest inside the USSR, model outputs and optimization logs and model comparison.

Supplementary Table 2 (download XLSX )

Input data and results for all cities of interest outside the USSR, model outputs and optimization logs.

Supplementary Table 3 (download CSV )

Full list of marked newsreel outlines used in the analysis.

Supplementary Table 4 (download ZIP )

Comparison of outline annotations for 12 selected cities done by two different human annotators.

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Tamm, M.V., Oiva, M., Mukhina, K.D. et al. City representation in Soviet propaganda and geographical biases in cultural data. Nat Cities 3, 146–154 (2026). https://doi.org/10.1038/s44284-025-00380-1

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