El problema de la vivienda en los medios: El caso de la prensa generalista española (2007-2025)

Ángel Gallardo-Agudo; Manuel García-Borrego; David Becerra-Alonso

doi:10.4185/rlcs-2027-2691

Authors

Ángel Gallardo-Agudo Universidad de Málaga https://orcid.org/0009-0007-5189-3485
Manuel García-Borrego Universidad de Málaga https://orcid.org/0000-0001-6207-8741
David Becerra-Alonso Uni https://orcid.org/0000-0001-5174-7743

DOI:

https://doi.org/10.4185/rlcs-2027-2691

Keywords:

agenda-setting, sentiment analysis, Spain, journalism, topic modeling, media coverage, housing

Abstract

Introduction: Access to housing is one of the main concerns of Spanish citizens. In this context, the present study aims to describe how the housing issue has been represented in Spain’s mainstream press between 2007 and 2025, while also comparing and classifying the coverage of leading newspapers. Methodology: To achieve this objective, a content analysis was conducted on 21,618 articles from 13 Spanish newspapers, applying topic modeling (BERTopic) and sentiment analysis (twitter-XLM-roBERTa), which enabled a hierarchical clustering of these outlets. Results and discussion: The findings confirm a clear predominance of economic issues until 2020, when political and social themes —previously marginal— began to emerge on the agenda, introducing new ways of framing housing. Moreover, except for explicitly progressive outlets (eldiario.es, Público, and El HuffPost), which tend to prioritize social issues such as the housing crisis and protests, most newspapers exhibit predominantly economic coverage. Conclusions: This study is the first within the field of communication sciences to apply these computational techniques to the analysis of media coverage of housing, while also handling such a large corpus in the context of the Spanish press. The results show that only when the housing problem reached such a magnitude that it became unavoidable did the Spanish mainstream media system begin to problematize the phenomenon.

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Author Biographies

Ángel Gallardo-Agudo, Universidad de Málaga

Doctoral researcher with an FPU contract in the Department of Journalism at the University of Málaga. In 2023, he was awarded a research collaboration grant by the Spanish Ministry of Education, and in 2024, a research initiation grant funded by the University of Málaga. His research has focused on media discourse and videogame journalism, with publications in these areas. In 2025, he received the Extraordinary Award of the Master’s Degree in Research on Media, Audiences and Professional Practice in Europe (University of Málaga).

Manuel García-Borrego, Universidad de Málaga

Associate Professor in the Department of Journalism at the University of Málaga. He completed his doctoral dissertation with an FPU predoctoral fellowship awarded by the Spanish Ministry of Education between 2016 and 2020 (Outstanding cum laude, International Doctorate distinction, and Extraordinary Doctoral Award). He has published 33 articles in impact-indexed journals (26 indexed in JCR/Scopus), 20 book chapters with Q1 publishers according to the SPI ranking, and more than 50 papers presented at international conferences. He has also undertaken funded research stays at the University of Bergen (Norway) and Universitat Jaume I (Castelló), and has worked as an Interim Substitute Lecturer at the University of Seville. In 2020, he received the University of Málaga Teaching Award.

David Becerra-Alonso, Uni

David Becerra Alonso received his B.Sc. degree in physics from Universidad de Córdoba, Spain, in 2005, where he specialized in the simulation of physical systems, and his Ph.D. from the School of Computing at University of the West of Scotland, Paisley, U.K., in 2010, where he was involved on dynamical chaotic systems. He also has a master’s degree in bioinformatics from Universidad Internacional de Andalucía, Seville, Spain. He is currently an Associate Professor at Universidad Loyola Andalucía, Spain. His current research interests include dynamical systems, emergent collective behavior, and machine learning techniques and heuristics.

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