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。AI 中文总结
该研究提出融合ELM与TPB的多分支框架，结合Transformer语义与心理线索，检测健康虚假信息并引入CPS辅助传播风险推理，在三个基准数据集上表现优异，提升了检测性能。
AI 中文摘要
本文提出一种多分支融合框架，用于检测和分析在线社交网络（OSNs）中健康虚假信息的传播特征。该模型基于精细可能性模型（ELM）和计划行为理论（TPB），在统一多任务架构中融合基于Transformer的语义信息、修辞线索、立场表示以及受心理学启发的代理指标。除二分类任务外，本文还引入认知传播得分（CPS），这是一种可解释的事后辅助得分，由从文本中提取的心理学相关线索计算得到，这些线索包括论证复杂性、情感强度和内容衍生的传播潜力，用于在互动真实值不完整或不可用时辅助传播风险推理。在Constraint、COVID-19_FNIR和Monkeypox三个基准数据集上的实验显示出出色的分类性能，在COVID-19_FNIR数据集上的ROC-AUC高达0.9999；当有互动监督时，面向传播的排序任务在Monkeypox数据集上达到近乎完美的一致性（斯皮尔曼相关系数ρ=0.9952），在COVID-19_FNIR数据集上基于代理监督也达到了相近的高排序一致性（ρ=0.9954）。与代表性文献基线相比，该融合模型在Constraint和COVID-19_FNIR数据集上的检测性能有所提升，而Monkeypox数据集更具挑战性，反映出领域和信号特异性的差异。消融分析进一步表明，心理学分支和修辞分支提供了超出语义嵌入的互补增益。总体而言，该框架将认知理论与神经建模相结合，以提升透明度并支持可扩展的虚假信息监测，未来工作需验证CPS与以人为中心的传播判断的一致性。
英文摘要
This paper presents a multi-branch fusion framework for detecting and characterising the propagation of health misinformation in online social networks (OSNs). Grounded in the Elaboration Likelihood Model (ELM) and the Theory of Planned Behaviour (TPB), the model fuses transformer-based semantics with rhetorical cues, stance representations, and psychologically motivated proxies in a unified multi-task architecture. In addition to binary classification, we introduce the Cognitive Propagation Score (CPS), an interpretable post-hoc auxiliary score computed from psychologically motivated, text-derived cues capturing argument complexity, emotional intensity, and content-derived virality potential, to support diffusion-risk reasoning when engagement ground truth is incomplete or unavailable. Experiments on three benchmark datasets, Constraint, COVID--19\_FNIR, and Monkeypox, show strong classification performance, achieving ROC--AUC up to 0.9999 on COVID--19\_FNIR, while propagation-oriented ranking achieves near-perfect agreement when engagement-derived supervision is available (Monkeypox, Spearman's $ρ= 0.9952$) and similarly high ranking alignment under proxy-based supervision on COVID--19\_FNIR ($ρ= 0.9954$). Compared with representative literature baselines, the fusion model improves detection on Constraint and COVID--19\_FNIR, while Monkeypox remains more challenging, reflecting domain- and signal-specific differences. Ablation analysis further indicates that psychological and rhetorical branches provide complementary gains beyond semantic embeddings. Overall, the framework bridges cognitive theory and neural modelling to improve transparency and to support scalable misinformation monitoring, with future work required to validate CPS against human-centred diffusion judgements.
Comments1 figure, 8 tables
