British news media representations of mpox during the 2022 and 2024 outbreaks: a mixed-methods analysis using corpus linguistics
Notice bibliographique
Résumé
Abstract Background Since 2022, over 100,000 people across 100 countries have been diagnosed with mpox (formerly monkeypox, renamed by the World Health Organization (WHO) in November 2022). News media plays a central role in outbreaks, disseminating information and shaping public discourse. Corpus linguistic approaches to massive language datasets can reveal how such outbreaks are represented but remain under-used in public health. Using these methods, we investigated representations of mpox in British news media during the 2022 and 2024 outbreaks. Methods We analysed the 83-billion-word English Trends corpus in SketchEngine, quantifying use of “ monkeypox ” and “ mpox” in 2022–2024, and applying Corpus-Assisted Discourse Studies to compare news media content from 2022 and 2024 (peak incidence periods), comprising 1.2 billion and 500 million words, respectively. Using corpus linguistic tools, we explored the contexts in which mpox and monkeypox occurred, assessing shifts in representation. Findings Monthly use of “monkeypox” peaked at 0.07 occurrences per million words (n=6591) in May 2022, dropping by 99.8% by November 2022. Grammatical and lexical analysis of 2022 reporting found frequent attribution blame for transmission, particularly to gay and bisexual men who have sex with men (GBMSM). In 2024, coverage adopted more neutral language, largely avoiding stigmatisation. Interpretation UK news media reporting on mpox shifted from stigmatising language in 2022, often targeting GBMSM, to more neutral and inclusive coverage in 2024. The WHO-endorsed nomenclature change may have contributed, illustrating the impact of such interventions. This study demonstrates the value of corpus methods in tracking linguistic representations of infectious disease outbreaks. Funding This work is part of the VERDI project (101045989) which is funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. BM, MLP and ST are partly funded through a Wellcome Accelerator Award held by ST (316319/Z/24/Z). Research in context Evidence before this study We reviewed existing literature on media representations of mpox, and other infectious diseases, focusing on stigma, framing, and public perception. We searched academic databases, including studies that examined media discourse and linguistic framing, and those using qualitative or corpus-based methods. While some explored stigma in mpox media coverage, few studies applied computational (computer-based) linguistic analysis to large-scale media datasets, or compared media narratives across different outbreak periods. Added value of this study This study is the first to use a ‘big data’ approach to explore representations of mpox. It uses corpus linguistic methods to analyse over two billion words of British news media content across two mpox outbreaks. It reveals a shift from stigmatising language in 2022—often targeting specific communities—to more neutral and inclusive reporting in 2024. The findings demonstrate how media language evolves in response to public health guidance and highlight the potential of corpus linguistic methods to uncover patterns in public discourse. Implications of all the available evidence Media language plays a powerful role in shaping public understanding and attitudes during health emergencies. This study shows that changes in terminology and framing can reduce stigma and improve public health communication. These insights support the need for proactive media guidance and the use of linguistic analysis to inform future policy, practice, and research in outbreak response and health communication.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,007 | 0,010 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».