Fake news has emerged as a pervasive problem within Online Social Networks, leading to a surge of research interest in this area. Understanding the dissemination mechanisms of fake news is crucial in comprehending the propagation of disinformation/misinformation and its impact on users in Online Social Networks. This knowledge can facilitate the development of interventions to curtail the spread of false information and inform affected users to remain vigilant against fraudulent/malicious content. In this paper, we specifically target the Twitter platform and propose a Multivariate Hawkes Point Processes model that incorporates essential factors such as user networks, response tweet types, and user stances as model parameters. Our objective is to investigate and quantify their influence on the dissemination process of fake news. We derive parameter estimation expressions using an Expectation Maximization algorithm and validate them on a simulated dataset. Furthermore, we conduct a case study using a real dataset of fake news collected from Twitter to explore the impact of user stances and tweet types on dissemination patterns. This analysis provides valuable insights into how users are influenced by or influence the dissemination process of disinformation/misinformation, and demonstrates how our model can aid in intervening in this process.

翻译：虚假新闻已成为在线社交网络中的普遍问题，引发了该领域的研究热潮。理解虚假新闻的传播机制对于掌握不实/误导信息的扩散规律及其对在线社交网络用户的影响至关重要。这一认知有助于开发遏制虚假信息传播的干预措施，并提醒受影响用户对欺诈/恶意内容保持警惕。本文以Twitter平台为研究对象，提出一种将用户网络、推文响应类型及用户立场等关键因素作为模型参数的多元霍克斯点过程模型，旨在探究并量化这些因素对虚假新闻传播过程的影响。我们通过期望最大化算法推导参数估计表达式，并在模拟数据集上验证其有效性。进一步地，基于从Twitter收集的真实虚假新闻数据集开展案例研究，分析用户立场与推文类型对传播模式的影响。该分析为理解用户如何影响及被不实/误导信息传播过程所影响提供了重要见解，并展示本模型如何协助干预该传播过程。