Neural Network Analysis of Risk Perception around COVID-19 Disseminated in Socio-digital Networks
Year: 2025
Type: article
Study design: Observational Study
Abstract: A study on environmental risk perception analyzed the relationship between categories such as prevention, planning, inevitability, emotionality, improvisation and confrontation in a population exposed to environmental risks. A total of 709 residents of a town in central Mexico, selected according to their degree of exposure, were surveyed, considering people over 18 years of age as inclusion criteria and excluding people with temporary residence or conditions that made their participation difficult. A quantitative and cross-sectional methodological design was employed, using neural networks to analyze centrality, connectivity and grouping of the categories. The results showed that planning and prevention are central nodes, reflecting their relevance in risk perception, while emotionality and improvisation played mediating roles, and inevitability and confrontation were located in peripheral positions. The empirical findings were consistent with the theoretical models, indicating that the relationships observed in the network correspond to established patterns of risk perception and emotional management. We conclude by rejecting the hypothesis of significant differences between the theoretical and empirical relationships, confirming the validity of the central categories as structural elements in environmental risk management.
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4408928081