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Does Local News Stay Local?: Online Content Shifts in Sinclair-Acquired Stations

Paper · 2026 · Miriam Wanner, Sophia Hager and Anjalie Field

Summary

The paper examines how local TV newsrooms change their content after being acquired by the Sinclair Broadcast Group. Local news is generally trusted across party lines, which makes nationalized or polarized coverage a concern. Prior research relied on broadcasts or station websites. This study instead builds a new dataset of automatically generated YouTube closed captions from eight local stations that had YouTube channels before Sinclair bought them. The data spans about sixteen years, and Fox News and CNN serve as national reference points. The authors compare content before and after each purchase using three methods: log-odds word comparisons, Structural Topic Models with ownership status and date as covariates, and word-embedding analyses of politicized terms. All methods point the same way. After acquisition, stations shift toward national and polarizing political stories and away from community fare such as cooking and local sports. Their use of charged vocabulary also comes to resemble that of national cable outlets. However, no consistent rightward tilt toward Fox relative to CNN emerges. The work suggests one way Sinclair ownership could shape viewers' political attitudes, and it establishes YouTube as a useful source for media research.

Key points

  • The dataset covers 8 geographically varied stations with at least 2,000 videos each:
    • WSBT-TV, NBC Montana, WCTI-TV, WCYB, WLUK-TV, WJAR, WGXA and WJLA-TV (7NewsDC).
    • Four were purchased in 2014, one in 2016 and three in 2017.
    • The @CNN and @FoxNews YouTube channels serve as comparisons.
  • The analyses combine three tools:
    • log-odds ratios with informative priors (Monroe et al., 2008);
    • STM (Roberts et al.), chosen over neural topic models for its stability;
    • comparisons of word contexts using embeddings, drawing on Garg et al. (2018) and the embedding approach of Mikolov et al. (2013).
  • The authors trained five STM models with 30 topics each:
    • Four use news affiliation (before purchase, after purchase, CNN, Fox) plus a b-spline date covariate, on all data or on 2014, 2015 or 2016 alone.
    • The fifth compares Sinclair-owned with non-Sinclair stations.
  • Read through communications theory, the observed shifts mainly reflect agenda-setting, with only limited signs of framing changes.
  • The study is observational, so causality is not established. Other limitations include uneven data across stations and years, and subjective choices about which words count as politicized.

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

🎓Johns Hopkins UniversityBaltimore · United States240 works · 96,487 citations

 Fields, topics and concepts

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Social Sciences · 1,772 worksSociology and Political Science · 577 worksCommunication · 97 works

Cite

Cite as Miriam Wanner, Sophia Hager and Anjalie Field. 2026. Does Local News Stay Local?: Online Content Shifts in Sinclair-Acquired Stations. Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.nlpcss-1.3

@inproceedings{wanner2026does,
  title = {{Does Local News Stay Local?: Online Content Shifts in Sinclair-Acquired Stations}},
  author = {Miriam Wanner, Sophia Hager and Anjalie Field},
  booktitle = {Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science},
  year = {2026},
  doi = {10.18653/v1/2026.nlpcss-1.3},
  url = {https://doi.org/10.18653/v1/2026.nlpcss-1.3}
}

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