analiza aiqbench
This research proposes a retrieval-augmented transformer framework to verify health misinformation using localized evidence from WHO and Nigerian CDC sources. While current results show moderate accuracy due to limited dataset size, the study highlights critical challenges in adapting global AI models for resource-constrained settings during disease outbreaks. The work underscores the necessity of context-aware knowledge bases rather than relying solely on generic biomedical resources for effective fact-checking.
An Evidence-Grounded Retrieval-Augmented Transformer Framework for Health Misinformation Verification
arXiv:2608.02310v1 Announce Type: new Abstract: The rapid spread of false and misleading health information through digital platforms has become a major public health challenge, particularly during infectious disease outbreaks where delayed verification can influence public behaviour and hinder effective disease control. Although recent advances in automated health misinformation detection have shown encouraging results, most existing approaches rely heavily on global biomedical resources and often fail to capture the local context needed to verify claims in developing countries. This study presents a retrieval-augmented transformer framework designed to verify health-related claims using trusted evidence from the World Health Organization and the Nigeria Centre for Disease Control and Prevention. The framework combines semantic evidence retrieval with transformer-based classification to determine whether a claim is true, false, or misleading. To evaluate the proposed approach, a manually annotated dataset of 67 verified health claims covering coronavirus disease, Lassa fever, cholera, measles, and monkeypox was compiled from Nigerian fact-checking sources. Three transformer models and a retrieval-augmented configuration were evaluated. The Bidirectional Encoder Representations from Transformers model achieved the best performance, with an accuracy of 71% and a weighted F1-score of 0.66. Although retrieval augmentation did not improve classification performance because the current evidence repository was limited in size and coverage, the findings highlight the importance of comprehensive and authoritative knowledge sources for reliable health misinformation verification. The proposed framework provides a practical foundation for developing context-aware and evidence-driven health misinformation verification systems for Nigeria and other resource-constrained settings.
Abstract:The rapid spread of false and misleading health information through digital platforms has become a major public health challenge, particularly during infectious disease outbreaks where delayed verification can influence public behaviour and hinder effective disease control. Although recent advances in automated health misinformation detection have shown encouraging results, most existing approaches rely heavily on global biomedical resources and often fail to capture the local context needed to verify claims in developing countries. This study presents a retrieval-augmented transformer framework designed to verify health-related claims using trusted evidence from the World Health Organization and the Nigeria Centre for Disease Control and Prevention. The framework combines semantic evidence retrieval with transformer-based classification to determine whether a claim is true, false, or misleading. To evaluate the proposed approach, a manually annotated dataset of 67 verified health claims covering coronavirus disease, Lassa fever, cholera, measles, and monkeypox was compiled from Nigerian fact-checking sources. Three transformer models and a retrieval-augmented configuration were evaluated. The Bidirectional Encoder Representations from Transformers model achieved the best performance, with an accuracy of 71% and a weighted F1-score of 0.66. Although retrieval augmentation did not improve classification performance because the current evidence repository was limited in size and coverage, the findings highlight the importance of comprehensive and authoritative knowledge sources for reliable health misinformation verification. The proposed framework provides a practical foundation for developing context-aware and evidence-driven health misinformation verification systems for Nigeria and other resource-constrained settings.
17 pages, 2 figures, To appear in the Reimagining knowledge systems for digital transformation and sustainable development in the 21st century conference 2026, faculty of social sciences education. Federal University of Education, Zaria
Computation and Language (cs.CL)
arXiv:2608.02310 [cs.CL]
(or arXiv:2608.02310v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.02310 arXiv-issued DOI via DataCite (pending registration)
Text integral sub licența deschisă a publicației · 289 cuvinte
Subiecte conexe
Memory-First Fact-Checking: A Knowledge-Graph-Grounded Multi-Agent System for Misinformation Detection
arXiv · 1 sept. 2026
Modernizing and scaling support operations with generative AI on AWS
AWS · 2 sept. 2026
BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment
arXiv · 8 oct. 2026
Building Legal Reward Models for Grounding and Abstention
arXiv · 16 sept. 2026