Social Reinforcement Learning to Combat Fake News Spread.

In this work, we develop a social reinforcement learning approach to combat the spread of fake news. Specifically, we aim to learn an intervention model to promote the spread of true news in a social network—in order to mitigate the impact of fake news. We model news diffusion as a Multivariate Hawkes Process (MHP) and make interventions that are learnt via policy optimization. The key insight is to estimate the response a user will get from the social network upon sharing a post, as it indicates her impact on diffusion, and will thus help in efficient allocation of incentive. User responses also depend on political bias and peer influence, which we model as a second MHP, interleaving it with the news diffusion process. We evaluate our model on semi-synthetic and real-world data. The results demonstrate that our proposed model outperforms other alternatives that do not consider estimates of user responses and political bias when learning how to allocate incentives.