将说服理论整合至社交媒体上健康错误信息传播的流行病学建模中
Integrating Persuasion Theory into the Epidemiological Modelling of Health Misinformation Spread on Social Media
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中文总结 AI 辅助
该研究将精细可能性模型融入扩展的SIR流行病学模型，提出ELM-SIRMMM框架，在多数据集上验证其提升健康错误信息传播建模的精度与动态真实性，揭示需动态心理输入保障建模功能真实性。
中文摘要 AI 辅助
本研究提出一种流行病学与行为学混合框架，用于模拟社交媒体上健康错误信息的传播。我们将经典的易感-感染-恢复（SIR）模型扩展为六隔间结构（SIRMMM），纳入易感错误信息者（MS）、感染错误信息者（MI）、恢复错误信息者（MR）隔间，以更好反映错误信息的生命周期动态。为解释个体层面的行为差异，我们整合精细可能性模型（ELM）的心理信号扩展SIRMMM模型，包括情感极性、互动指标与认知努力，这些因素可动态调节错误信息传播率，形成ELM-SIRMMM框架。模型参数采用FibVID数据集估计，该数据集捕捉Twitter上的COVID-19错误信息；泛化性在另外两个数据集上测试：MC-Fake（情感类错误信息）与Monant（通用健康类错误信息）。结果显示，ELM-SIRMMM模型提升了预测精度与动态真实性：在FibVID上，其均方根误差（RMSE）降低5.5%，将错误信息峰值从第150天延迟至第160天，峰值流行率从6%提升至7%；在MC-Fake上，它准确再现闪谣模式，第45天感染38%的用户，实现97%的错误信息恢复，同时保持模型精度；而Monant数据集的行为信号变异性极小，仅带来边际效益，峰值提升3%，仍有57%的用户保持易感。这些发现表明，仅结构细化不足，错误信息传播建模的功能真实性需要随时间和情境有意义变化的动态心理输入。
英文摘要
This study presents a hybrid epidemiological and behavioural framework to simulate the spread of health misinformation on social media. We extend the classical Susceptible--Infected--Recovered (SIR) model to a six-compartment structure (SIRMMM), incorporating Misinformed Susceptible (MS), Misinformed Infected (MI), and Misinformed Recovered (MR) compartments to better reflect the dynamics of the misinformation lifecycle. To account for individual-level behavioural variation, we extend the SIRMMM model by integrating psychological signals from the Elaboration Likelihood Model (ELM), including sentiment polarity, engagement metrics, and cognitive effort, which dynamically modulate the misinformation transmission rate, yielding the ELM-SIRMMM framework. Model parameters were estimated using the FibVID dataset, which captures COVID-19 misinformation on Twitter. Generalisability was tested on two additional datasets: MC-Fake (emotional misinformation) and Monant (general health misinformation). Results show that the ELM-SIRMMM model enhances both predictive accuracy and dynamic realism. On FibVID, it decreases RMSE by 5.5%, delays the misinformation peak from day 150 to day 160, and increases its peak prevalence from 6% to 7%. On MC-Fake, it accurately reproduces a flash-rumour pattern, infecting 38% of users by day 45 and achieving 97% misinformation recovery, all while maintaining model accuracy. In contrast, minimal behavioural signal variability in the Monant dataset leads to marginal benefit, with only a 3% peak and 57% of users remaining susceptible. These findings suggest that structural elaboration alone is insufficient. Functional realism in modelling misinformation spread requires dynamic psychological inputs that vary meaningfully across time and contexts.
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。
