PDF(Pubmed)
Abstract：
We develop a simulation framework for studying misinformation spread within online social networks that blends agent-based modeling and natural language processing techniques. While many other agent-based simulations exist in this space, questions over their fidelity and generalization to existing networks in part hinder their ability to drive policy-relevant decision making. To partially address these concerns, we create a \'digital clone\' of a known misinformation sharing network by downloading social media histories for over ten thousand of its users. We parse these histories to both extract the structure of the network and model the nuanced ways in which information is shared and spread among its members. Unlike many other agent-based methods in this space, information sharing between users in our framework is sensitive to topic of discussion, user preferences, and online community dynamics. To evaluate the fidelity of our method, we seed our cloned network with a set of posts recorded in the base network and compare propagation dynamics between the two, observing reasonable agreement across the twin networks over a variety of metrics. Lastly, we explore how the cloned network may serve as a flexible, low-cost testbed for misinformation countermeasure evaluation and red teaming analysis. We hope the tools explored here augment existing efforts in the space and unlock new opportunities for misinformation countermeasure evaluation, a field that may become increasingly important to consider with the anticipated rise of misinformation campaigns fueled by generative artificial intelligence.
摘要：
我们开发了一个模拟框架，用于研究在线社交网络中传播的错误信息，该框架融合了基于代理的建模和自然语言处理技术。虽然在这个空间中存在许多其他基于代理的模拟，关于他们对现有网络的保真度和泛化的问题部分阻碍了他们推动政策相关决策的能力。为了部分解决这些问题，我们通过下载超过一万名用户的社交媒体历史记录，创建了一个已知的错误信息共享网络的“数字克隆”。我们解析这些历史，以提取网络的结构，并对信息在其成员之间共享和传播的微妙方式进行建模。与此领域中的许多其他基于代理的方法不同，在我们的框架中，用户之间的信息共享对讨论的话题很敏感，用户偏好，和在线社区动态。为了评估我们方法的保真度，我们用一组记录在基础网络中的帖子来播种我们的克隆网络，并比较两者之间的传播动态，在各种指标上观察孪生网络之间的合理协议。最后,我们探索克隆网络如何作为一个灵活的，用于错误信息对策评估和红色团队分析的低成本测试平台。我们希望这里探索的工具可以增强该领域的现有努力，并为错误信息对策评估提供新的机会，随着生成人工智能推动的错误信息活动的预期兴起，这一领域可能变得越来越重要。
