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Fake News Mitigation via Point Process Based Intervention
Mehrdad Farajtabar · Jiachen Yang · Xiaojing Ye · Huan Xu · Rakshit Trivedi · Elias Khalil · Shuang Li · Le Song · Hongyuan Zha

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #79

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 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.

Author Information

Mehrdad Farajtabar (Georgia Tech)
Jiachen Yang (Georgia Institute of Technology)
Xiaojing Ye (Georgia State University)
Huan Xu (Georgia Tech)
Rakshit Trivedi (Georgia Institute of Technology)
Elias Khalil (Georgia Tech)
Shuang Li (Georgia Tech)
Le Song (Georgia Institute of Technology)
Hongyuan Zha (Georgia Institute of Technology)

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