News Diversity Across Platforms. A Large-Scale Analysis of the Norwegian News Media Market

This article examines how topic diversity in Norwegian news media varies across proprietary and social media platforms. Drawing on a comprehensive dataset of 224,905 articles published in 2023 by 22 Norwegian news outlets, and corresponding posts on Facebook, Instagram, and TikTok, the study employs Latent Dirichlet Allocation (LDA) topic modeling to identify and compare thematic patterns. Results reveal systematic adaptations to platform-specific logics: while the outlets’ online news sites maintain broad topical diversity, they favour politics and human-interest stories on Facebook, personalised and entertainment-oriented content on Instagram, and sensationalist, event-driven topics on TikTok. Statistical diversity measures confirm that the outlets’ social media posts display lower topical diversity than their online articles. These findings demonstrate that editorial strategies align with distinct platform logics, contributing to a segmented news environment. The study argues that such differentiation has implications for the democratic role of journalism, as audiences encounter increasingly platform-tailored representations of news.

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Publication Details

Journal
Digital Journalism
Published
2026-07-23
DOI
https://doi.org/10.1080/21670811.2026.2706052
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

News Diversity Across Platforms. A Large-Scale Analysis of the Norwegian News Media Market

Gunhild Ring Olsen, Håvard Kiberg, Bente Kalsnes, Steen Steensen et al.
Digital Journalism
Computational and Text Analysis Methods
article

News Diversity Across Platforms. A Large-Scale Analysis of the Norwegian News Media Market

Gunhild Ring Olsen, Håvard Kiberg, Bente Kalsnes, Steen Steensen, Lasha Kavtaradze
article en

Abstract

This article examines how topic diversity in Norwegian news media varies across proprietary and social media platforms. Drawing on a comprehensive dataset of 224,905 articles published in 2023 by 22 Norwegian news outlets, and corresponding posts on Facebook, Instagram, and TikTok, the study employs Latent Dirichlet Allocation (LDA) topic modeling to identify and compare thematic patterns. Results reveal systematic adaptations to platform-specific logics: while the outlets’ online news sites maintain broad topical diversity, they favour politics and human-interest stories on Facebook, personalised and entertainment-oriented content on Instagram, and sensationalist, event-driven topics on TikTok. Statistical diversity measures confirm that the outlets’ social media posts display lower topical diversity than their online articles. These findings demonstrate that editorial strategies align with distinct platform logics, contributing to a segmented news environment. The study argues that such differentiation has implications for the democratic role of journalism, as audiences encounter increasingly platform-tailored representations of news.

Digital Journalism
Høyskolen Kristiania (NO)
Openalex Percentile: Top 2%
Computational and Text Analysis Methods
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