论文标题
从基于代理的模型到假新闻的宏观描述：能力在数据驱动应用中的作用
From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications
论文作者
论文摘要
假新闻传播的目的是操纵个人对事实的看法，现在在许多民主社会中被认为是一个主要问题。然而，迄今为止，关于假新闻如何在社交网络上传播，个人教育水平的影响，假新闻有效地影响公众舆论以及哪些干预措施在减轻其效果方面可能成功的何种干预措施时，几乎几乎没有理解。在本文中，从最近引入的动力学多代理模型具有前两位作者的能力，我们建议通过在平均场近似中的社会封闭概念来得出减少阶数模型，该概念依赖于其在动力学理论的经典流体动力学封闭中。这种方法允许获得简化的模型，在这些模型中，代理的能力和学习在动态中保持作用，同时，此类模型的结构更适合与数据驱动的应用程序相连。描述和讨论了不同基于Twitter的测试用例的示例。
Fake news spreading, with the aim of manipulating individuals' perceptions of facts, is now recognized as a major problem in many democratic societies. Yet, to date, little has been understood about how fake news spreads on social networks, what the influence of the education level of individuals is, when fake news is effective in influencing public opinion, and what interventions might be successful in mitigating their effect. In this paper, starting from the recently introduced kinetic multi-agent model with competence by the first two authors, we propose to derive reduced-order models through the notion of social closure in the mean-field approximation that has its roots in the classical hydrodynamic closure of kinetic theory. This approach allows to obtain simplified models in which the competence and learning of the agents maintain their role in the dynamics and, at the same time, the structure of such models is more suitable to be interfaced with data-driven applications. Examples of different Twitter-based test cases are described and discussed.
