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A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions
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Meng Su,
Petr Hajek ,
Mohammad Abedin
Expert Systems with Applications, Volume: 298, Issue: Part B, Start page: 129583
Swansea University Author:
Mohammad Abedin
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DOI (Published version): 10.1016/j.eswa.2025.129583
Abstract
Predicting public opinion trends during major infectious disease outbreaks is critical for guiding effective public health responses. However, predicting public opinion remains challenging because it is influenced by socio-economic, psychological, and media factors. This paper presents a novel frame...
| Published in: | Expert Systems with Applications |
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| ISSN: | 0957-4174 1873-6793 |
| Published: |
Elsevier BV
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70370 |
| Abstract: |
Predicting public opinion trends during major infectious disease outbreaks is critical for guiding effective public health responses. However, predicting public opinion remains challenging because it is influenced by socio-economic, psychological, and media factors. This paper presents a novel framework for predicting public opinion trends related to significant infectious diseases, with a focus on COVID-19 as a case study. The proposed framework identifies the key factors influencing public opinion development and enables both point and interval predictions. The framework uses information ecology theory and applies the NSGA-II algorithm to select the features that best drive public opinion trends. By incorporating this framework, accurate point forecasts are produced alongside prediction intervals, effectively quantifying the uncertainty inherent in public opinion dynamics. This approach minimizes the quality-driven loss function to generate precise prediction intervals, providing decision-makers with critical insights into public opinion fluctuations during epidemics. The results offer valuable, real-time public sentiment warnings, supporting timely and effective interventions in epidemic prevention and control efforts. |
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| Keywords: |
Public opinion prediction; NSGA-II algorithm; Feature selection; Infectious diseases; Interval prediction |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Swansea University |
| Issue: |
Part B |
| Start Page: |
129583 |