Fake News Mitigation via Point Process Based Intervention
This addresses fake news mitigation for social media platforms, but it is incremental as it builds on existing reinforcement learning and point process methods.
The authors tackled the problem of fake news spread in social networks by developing a multistage intervention framework that combines reinforcement learning with a point process model, showing promising performance in real-time Twitter experiments and outperforming alternatives on synthetic datasets.
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. We develop a policy iteration method unique to the multivariate networked point process, with the goal of optimizing the actions for maximal total reward under budget constraints. Our method shows promising performance in real-time intervention experiments on a Twitter network to mitigate a surrogate fake news campaign, and outperforms alternatives on synthetic datasets.