使用 Drift Diffusion Models 量化在线社交网络上（错误）信息分享中的说服力、谨慎性和先验信念的影响
Quantifying the impact of persuasiveness, cautiousness and prior beliefs in (mis)information sharing on online social networks using Drift Diffusion Models
AI总结：
本研究使用 Drift Diffusion Model 分析在线社交网络上（错误）信息分享的决策机制，发现理性思维可抑制分享误导性新闻的本能，并指出限制高连接用户关注者数量是遏制信息病毒式传播的有效策略。
摘要：
误导性新闻通讯可以塑造个人的认知，并对社会构成威胁；正如我们所目睹的那样，它降低了后续居家隔离令的严格性，并为抗击 COVID-19 带来了巨大挑战。在本研究中，我们研究（错误）信息的传播，重新分析在线分享的行为数据，并使用 Drift Diffusion Model (DDM) 分析决策机制。我们发现，受试者对分享误导性新闻表现出更强的本能倾向，但理性思维显著抑制了这种反应，尤其是对于更谨慎和年长的个体。在与 X、Mastodon 和 Facebook 具有相似特征的网络结构之上，我们使用基于智能体的模型将这种个体知识扩展到大规模场景，其中个体通过朋友接触（错误）信息，并以 DDM 驱动的概率分享（或不分享）内容。我们发现，这些在线社交网络的天然结构为任何新闻迅速成为病毒式传播提供了肥沃的土壤。然而我们发现，对于 X 的情况，限制连接最紧密用户的关注者数量被证明是一种适当且可行的遏制策略。
英文摘要：
Misleading newsletters can shape individuals' perceptions, and pose a threat to societies; as we witnessed by lowering the severity of follow-up stay-at-home orders and burdening a significant challenge to the fight against COVID-19. In this research, we study (mis)information spreading, reanalyzing behavioral data on online sharing, and analyzing decision-making mechanisms using the Drift Diffusion Model (DDM). We find that subjects display an increased instinctive inclination towards sharing misleading news, but rational thinking significantly curbs this reaction, especially for more cautious and older individuals. On top of network structures with similar characteristics as X, Mastodon, and Facebook, we use an agent-based model to expand this individual knowledge to a large scale where individuals are exposed to (mis)information through friends and share (or not) content with probabilities driven by DDM. We found that the natural shape of these social online networks provides a fertile ground for any news to rapidly become viral. Yet we have found that, for the case of X, limiting the number of followers of the most connected users proves to be an appropriate and feasible containment strategy.
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