  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.
