Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers
  via Self-Imitation Learning
Xiaofei Xu Ke Deng Michael Dann Xiuzhen Zhang
This study aims to minimize the influence of fake news on social networks by
deploying debunkers to propagate true news. This is framed as a reinforcement
learning problem, where, at each stage, one user is selected to propagate true
news. A challenging issue is episodic reward where the "net" effect of
selecting individual debunkers cannot be discerned from the interleaving
information propagation on social networks, and only the collective effect from
mitigation efforts can be observed. Existing Self-Imitation Learning (SIL)
methods have shown promise in learning from episodic rewards, but are
ill-suited to the real-world application of fake news mitigation because of
their poor sample efficiency. To learn a more effective debunker selection
policy for fake news mitigation, this study proposes NAGASIL - Negative
sampling and state Augmented Generative Adversarial Self-Imitation Learning,
which consists of two improvements geared towards fake news mitigation:
learning from negative samples, and an augmented state representation to
capture the "real" environment state by integrating the current observed state
with the previous state-action pairs from the same campaign. Experiments on two
social networks show that NAGASIL yields superior performance to standard GASIL
and state-of-the-art fake news mitigation models.