English(EN) A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation

新框架融合AI与心理学以检测健康错误信息

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种新颖的多分支融合框架，旨在检测和分析社交媒体上健康错误信息的传播。该模型将基于Transformer的语义分析与修辞线索和心理因素相结合，借鉴了可能性模型（Elaboration Likelihood Model）和计划行为理论（Theory of Planned Behaviour）等既有理论。除了简单的分类，该框架还引入了认知传播得分（Cognitive Propagation Score, CPS），利用可解释的、文本派生的指标（如论证复杂性和情感强度）来估计传播风险。在基准数据集上的实验表明，该框架在检测和传播排名方面均表现出高性能，优于现有文献基线。 AI

影响该框架通过将AI与心理学理论相结合，有望提高在线健康错误信息监控的透明度和可扩展性。

排序理由这是一篇详细介绍用于错误信息检测的新模型和框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao · 2026-09-02 04:00

一种多分支特征融合方法用于健康虚假信息检测与传播

arXiv:2609.00403v1 Announce Type: new Abstract: 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 Pla…

本文如何被排名

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一种多分支特征融合方法用于健康虚假信息检测与传播

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