用于健康虚假信息验证的基于证据的检索增强Transformer框架
An Evidence-Grounded Retrieval-Augmented Transformer Framework for Health Misinformation Verification
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- Yobe State University（约贝州立大学）
- Yobe AI Research Laboratory (YAIR Lab)（约贝人工智能研究实验室）
- Novosibirsk State University（新西伯利亚国立大学）
- College of Computer Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本研究针对发展中国家健康虚假信息验证的本地语境缺失问题，提出基于WHO和尼日利亚疾控中心证据的检索增强Transformer框架，经实验评估BERT模型表现最佳，为资源受限地区开发相关系统提供基础。
中文摘要 AI 辅助
虚假和误导性健康信息通过数字平台的快速传播已成为重大公共卫生挑战，尤其是在传染病暴发期间，延迟验证会影响公众行为并阻碍疾病有效防控。尽管近期自动化健康虚假信息检测取得了令人鼓舞的成果，但现有多数方法严重依赖全球生物医学资源，往往无法捕捉发展中国家验证主张所需的本地语境。本研究提出一种检索增强Transformer框架，旨在利用世界卫生组织（WHO）和尼日利亚疾病控制中心（NCDC）的可信证据验证健康相关主张。该框架将语义证据检索与基于Transformer的分类相结合，以确定主张为真、假或误导性。为评估所提方法，研究人员从尼日利亚事实核查来源汇编了包含67条经核实的健康主张的手动标注数据集，涵盖冠状病毒病、拉沙热、霍乱、麻疹和猴痘。研究评估了三种Transformer模型及一种检索增强配置，其中Bidirectional Encoder Representations from Transformers（BERT）模型表现最佳，准确率达71%，加权F1分数为0.66。尽管检索增强未提升分类性能，原因是当前证据库的规模和覆盖范围有限，但研究结果强调了全面且权威的知识来源对可靠健康虚假信息验证的重要性。所提框架为尼日利亚及其他资源受限环境开发语境感知、证据驱动的健康虚假信息验证系统提供了实用基础。
英文摘要
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.
