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Vol. 38. Núm. 1.
(Enero - Marzo 2024)
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Vol. 38. Núm. 1.
(Enero - Marzo 2024)
Original article
Examining the relationship between COVID-19 and suicide in media coverage through Natural Language Processing analysis
Hugo J. Belloa,1,
Autor para correspondencia
hugojose.bello@uva.es

Corresponding author.
, Nora Palomar-Ciriab,1, Celia Lozanoc, Carlos Gutiérrez-Alonsod, Enrique Baca-Garcíae,f,g,h,i,j,k,l,m,n,o
a Department of Applied Mathematics, Universidad de Valladolid, Soria, Spain
b Servicio de Psiquiatría, Complejo Asistencial Universitario de Soria, Soria, Spain
c Department of Data & Analytics, Bosonit, Logroño (La Rioja), Spain
d Department of Artificial Intelligence, Sermes CRO, Madrid, Spain
e Department of Psychiatry, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
f INSERM Unit 1061, Montpellier, France
g CHU Nimes and University of Montpellier, France
h Department of Psychiatry, Universidad Autónoma, Madrid, Spain
i Department of Psychiatry, Hospital Universitario Rey Juan Carlos, Móstoles, Spain
j Department of Psychiatry, Hospital General de Villalba, Madrid, Spain
k Department of Psychiatry, Hospital Universitario Infanta Elena, Valdemoro, Spain
l CIBERSAM (Centro de Investigación en Salud Mental), Carlos III Institute of Health, Madrid, Spain
m Universidad Católica del Maule, Talca, Chile
n Department of Psychiatry, Nimes University Hospital, Nimes, France
o Department of Big Data and Business Intelligence, Sermes CRO, Madrid, Spain
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Table 1. ML metrics of the models trained for news classification. The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The recall is the ratio tp / (tp + fn) where fn is the number of false negatives. The F1 score can be interpreted as a harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Abstract
Background and objectives

Suicide is a major public health concern, media can influence its awareness, contagion, and prevention. In this study, we evaluated the relationship between the COVID-19 pandemic and suicide in media coverage through Natural Language Processing analysis (NPL).

Methods

To study how suicide is depicted in news media, Artificial Intelligence and Big Data techniques were used to analyze news and tweets, to extract or classify the topic to which they belonged.

Results

A granger causality analysis showed with significant p-value that an increase in covid news at the beginning of the pandemic explains a later rise in suicide-related news. An analysis based on correlation and structural causal models show a strong relationship between the appearance of subjects “health” and “covid”, and also between “covid” and “suicide”.

Conclusions

Our analysis also uncovers that the inclusion of suicide-related news in the category health has grown since the outbreak of the pandemic. The COVID-19 pandemic has posed an inflection point in the way suicide-related news are reported. Our study found that the increased media attention on suicide during the COVID-19 pandemic may indicate rising social awareness of suicide and mental health, which could lead to the development of new prevention tools.

Keywords:
Suicide
Public health
Big data
Topic classification
Machine learning

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