Tong, J. orcid.org/0000-0001-6843-3177 (2025) Serving the public interest? A computational analysis of the topics of UK national newspaper coverage using Freedom of Information (FOI) requests between 2005 and 2023. Journalism Studies. ISSN 1461-670X
Abstract
The Freedom of Information (FOI) Act 2000 was enacted in the United Kingdom (UK) to improve government transparency and accountability. Requesting government information through FOI and using it in coverage support news media's watchdog role and the Act's full potential. However, the public interest served depends on the news story topics journalists pursue. This article employs Latent Dirichlet Allocation topic modelling and n-gram analysis to map the topics of 12,132 FOI news stories published by 10 UK national newspapers (and Sunday versions, if any) between 2005 and 2023. Despite fluctuations, FOI coverage increased overall, consistently addressing topics, such as “government and markets,” “NHS problems,” and “public funds and council spending issues,” shaping public agendas, and holding power accountable. Broadsheets and tabloids differed and overlaped in topics, while their political leanings made less difference. Although serving the public interest, some topics were sensational, such as “sexual crimes against children,” with some key areas like internal government communications and decision-making underrepresented. This research demonstrates FOI's key role in political communication, showing its potential to reform the political system and shape the relationship between the government, politicians, and news media. However, a balance is needed between fulfilling a watchdog role and avoiding sensationalism.
Metadata
Item Type: | Article |
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Authors/Creators: | |
Copyright, Publisher and Additional Information: | © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
Keywords: | Freedom of information; Latent Dirichlet allocation (LDA) topic modelling; n-gram analysis; machine learning; computational social science; political communication; journalism; The UK |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Journalism Studies (Sheffield) |
Funding Information: | Funder Grant number BRITISH ACADEMY (THE) SRG21\211038 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Jul 2025 10:41 |
Last Modified: | 17 Jul 2025 08:41 |
Published Version: | https://doi.org/10.1080/1461670x.2025.2518453 |
Status: | Published online |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/1461670x.2025.2518453 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229124 |