The formation of collective opinion is a complex phenomenon that results from the combined effects of mass media exposure and social influence between individuals. The present work introduces a model of opinion formation specifically designed to address risk judgments, such as attitudes towards climate change, terrorist threats, or children vaccination. The model assumes that people collect risk information from the media environment and exchange them locally with other individuals. Even though individuals are initially exposed to the same sample of information, the model predicts the emergence of opinion polarization and clustering. In particular, numerical simulations highlight two crucial factors that determine the collective outcome: the propensity of individuals to search for independent information, and the strength of social influence. This work provides a quantitative framework to anticipate and manage how the public responds to a given risk, and could help understanding the systemic amplification of fears and worries, or the underestimation of real dangers.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3875559 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0084592 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Application of the Omaha System-Based Continuous Care Model in Diabetes Health Management for Outpatients within the Framework of "Internet +".
Br J Hosp Med (Lond)
January 2025
Nursing Department, Zhang Ye People's Hospital Affiliated to Hexi University, Zhangye, Gansu, China.
Diabetes is a chronic lifelong condition that requires consistent self-care and daily lifestyle adjustments. Effective disease management involves regular blood glucose monitoring and ongoing nursing support. Inadequate education and poor self-management are key factors contributing to increased mortality among diabetic individuals.
View Article and Find Full Text PDFThe Association Between Adherence to the Mediterranean Diet and Depression and Anxiety Symptoms in University Students: The Mediating Role of Lean Mass and the Muscle Strength Index.
Nutrients
January 2025
Health and Social Research Center, Universidad de Castilla La-Mancha, 16071 Cuenca, Spain.
Background/objectives: recent studies have suggested that components typical of the Mediterranean diet (MedDiet) are associated with depression and anxiety prevention. In this sense, the main objective of this study was to analyse the associations between adherence to the MedDiet and depression and anxiety symptoms and to examine whether this relationship is mediated by lean mass and the muscle strength index (MSI).
Methods: a cross-sectional study (based on data obtained from the Nuts4Brain-Z study) was conducted from 2023-2024, involving 428 university students, aged 18-30 years, from a Spanish public university.
Balancing Performance and Health in Elite Hungarian Athletes: The Relationship Among Disordered Eating Risk, Body Composition, and Nutrition Knowledge.
Nutrients
January 2025
Department of Health and Nursing Sciences, Faculty of Health and Sport Sciences Széchenyi István University, H-9026 Gyor, Hungary.
Background: disordered eating (DE) and eating disorders (ED) can negatively impact athletes' health, wellbeing, and athletic performance.
Objective: this cross-sectional study aims to assess DE risk, body composition, and nutrition knowledge among elite Hungarian athletes.
Methods: DE risk was assessed using DESA-6H and EAT-26 scales, nutrition knowledge through the Abridged Nutrition for Sport Knowledge Questionnaire (A-NSKQ), and body composition with the OMRON BF511 device.
Social influence and consensus building: Introducing a q-voter model with weighted influence.
PLoS One
January 2025
Department of Physics, University of Calcutta, Kolkata, West Bengal, India.
We present a model of opinion formation where an individual's opinion is influenced by interactions with a group of agents. The model introduces a novel bias mechanism that favors one opinion, a feature not previously explored. In the absence of bias, the system reduces to a mean field voter model.
View Article and Find Full Text PDFMachine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.
PLoS One
January 2025
Faculty of Psychology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia.
Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adherence in tuberculosis patients, based on the increased level of liver enzymes.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!