神经复杂度是否提升健康错误信息检测？一个泄漏控制的跨语料基准
Does Neural Complexity Improve Health Misinformation Detection? A Leakage-Controlled Cross-Corpus Benchmark
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中文总结 AI 辅助
本研究通过泄漏控制的跨语料基准比较五种神经架构、集成模型和经典基线，发现模型复杂度无稳定优势，基准构建影响更大，为健康错误信息检测的模型选择提供了可复现依据。
中文摘要 AI 辅助
通常认为增加架构复杂度能提升健康错误信息的检测性能，然而当不同研究使用不同的语料库、预处理流程、数据划分和泄漏控制时，所报告的性能提升难以解释。本研究提供了一个受控的跨语料基准，涵盖五种紧凑型神经架构（1D-CNN、LSTM、BiLSTM、CNN-LSTM和CNN-BiLSTM）、一个软投票神经集成以及三个经典机器学习基线，使用COVID19-FNIR和CONSTRAINT数据集。在建模前应用了精确文本重复控制；所有神经模型采用统一的预处理和优化协议，并且神经模型的结果在三个随机种子上重复实验。在COVID19-FNIR上，深度集成取得了平均宏F1为0.9963和ROC-AUC为0.9994，而单个神经模型的宏F1在0.9945到0.9957之间。在CONSTRAINT上，集成模型取得了宏F1为0.9272和ROC-AUC为0.9811，而线性SVM取得了宏F1为0.9574和ROC-AUC为0.9931。架构排名在不同语料库间发生变化，简单的稀疏线性模型仍然具有很强的竞争力。研究结果表明，模型复杂度并不能提供稳定的性能优势，且基准构建可能主导架构选择。本研究为健康错误信息分类中基于证据的模型选择提供了一个可复现、泄漏控制的基础。
英文摘要
Increasing architectural complexity is often assumed to improve health misinformation detection, yet reported gains are difficult to interpret when studies use different corpora, preprocessing pipelines, data splits, and leakage controls. This study provides a controlled cross-corpus benchmark of five compact neural architectures (1D-CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM), a soft-voting neural ensemble, and three classical machine-learning baselines using COVID19-FNIR and CONSTRAINT. Exact-text duplicate controls were applied before modelling; all neural systems used a common preprocessing and optimisation protocol, and neural results were repeated across three random seeds. On COVID19-FNIR, the deep ensemble achieved a mean macro-F1 of 0.9963 and ROC-AUC of 0.9994, while individual neural models ranged from 0.9945 to 0.9957 macro-F1. On CONSTRAINT, the ensemble achieved macro-F1 of 0.9272 and ROC-AUC of 0.9811, whereas a linear SVM achieved macro-F1 of 0.9574 and ROC-AUC of 0.9931. Architecture rankings changed across corpora, and simple sparse linear models remained highly competitive. The findings show that model complexity does not provide a stable performance advantage and that benchmark construction can dominate architecture choice. The study contributes a reproducible, leakage-controlled basis for evidence-driven model selection in health misinformation classification
发表机构
- Manchester Metropolitan University（曼彻斯特城市大学）
机构由 AI 辅助整理，请以论文原文为准。
