Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint random walk over the state space. We use three Twitter datasets to evaluate our approach, one of them being a new COVID-19 dataset provided in this paper. Our approach shows fast convergence and increased valid information exposure. These results persisted independently of network structure, including networks with central nodes, where the latter could be the root of misinformation. Further, the LA obtained these results in a decentralized manner, facilitating distributed deployment in real-life scenarios.

减少社交媒体上的错误信息是一项尚未解决的挑战，尤其是因为信息传播的复杂性。为此，多元霍克斯过程 (MHP) 已成为一种基本工具，因为它们对社会网络动态进行建模，从而有助于缓解政策的执行和评估。在本文中，我们提出了一种新颖的基于轻量级干预的错误信息缓解框架，使用分散式学习自动机 (LA) 来控制 MHP。每个自动机与单个用户相关联，并通过与相应的 MHP 交互并在状态空间上执行联合随机游走来了解该用户应在何种程度上参与缓解策略。我们使用三个Twitter数据集来评估我们的方法，其中一个是本文提供的新 COVID-19 数据集。我们的方法显示出快速收敛和增加的有效信息暴露。这些结果独立于网络结构持续存在，包括具有中心节点的网络，后者可能是错误信息的根源。此外，LA 以分散的方式获得这些结果，便于在现实生活场景中进行分布式部署。