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A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions

Futian Weng Orcid Logo, Meng Su, Petr Hajek Orcid Logo, Mohammad Abedin Orcid Logo

Expert Systems with Applications, Volume: 298, Issue: Part B, Start page: 129583

Swansea University Author: Mohammad Abedin Orcid Logo

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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...

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Published in: Expert Systems with Applications
ISSN: 0957-4174 1873-6793
Published: Elsevier BV 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa70370
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spelling 2025-09-17T10:30:12.8200040 v2 70370 2025-09-17 A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2025-09-17 CBAE 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. Journal Article Expert Systems with Applications 298 Part B 129583 Elsevier BV 0957-4174 1873-6793 Public opinion prediction; NSGA-II algorithm; Feature selection; Infectious diseases; Interval prediction 1 3 2026 2026-03-01 10.1016/j.eswa.2025.129583 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-09-17T10:30:12.8200040 2025-09-17T10:14:54.4701454 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Futian Weng 0000-0002-7982-8729 1 Meng Su 2 Petr Hajek 0000-0001-5579-1215 3 Mohammad Abedin 0000-0002-4688-0619 4 70370__35098__936bd00e78d841778813d95c61d446e4.pdf 70370.VOR.pdf 2025-09-17T10:25:35.8894374 Output 3992676 application/pdf Version of Record true © 2025 The Author(s). This is an open access article distributed under the terms of the Creative Commons CC-BY license. true eng http://creativecommons.org/licenses/by/4.0/
title A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions
spellingShingle A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions
Mohammad Abedin
title_short A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions
title_full A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions
title_fullStr A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions
title_full_unstemmed A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions
title_sort A multi-objective framework for predicting public opinion trends on infectious diseases using NSGA-II and interval predictions
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Futian Weng
Meng Su
Petr Hajek
Mohammad Abedin
format Journal article
container_title Expert Systems with Applications
container_volume 298
container_issue Part B
container_start_page 129583
publishDate 2026
institution Swansea University
issn 0957-4174
1873-6793
doi_str_mv 10.1016/j.eswa.2025.129583
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
active_str 0
description 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.
published_date 2026-03-01T05:30:41Z
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score 11.089386

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