  Social networks are frequently polluted by rumors, which can be detected by
advanced models such as graph neural networks. However, the models are
vulnerable to attacks and understanding the vulnerabilities is critical to
rumor detection in practice. To discover subtle vulnerabilities, we design a
powerful attacking algorithm to camouflage rumors in social networks based on
reinforcement learning that can interact with and attack any black-box
detectors. The environment has exponentially large state spaces, high-order
graph dependencies, and delayed noisy rewards, making the state-of-the-art
end-to-end approaches difficult to learn features as large learning costs and
expressive limitation of graph deep models. Instead, we design domain-specific
features to avoid learning features and produce interpretable attack policies.
To further speed up policy optimization, we devise: (i) a credit assignment
method that decomposes delayed rewards to atomic attacking actions proportional
to the their camouflage effects on target rumors; (ii) a time-dependent control
variate to reduce reward variance due to large graphs and many attacking steps,
supported by the reward variance analysis and a Bayesian analysis of the
prediction distribution. On three real world datasets of rumor detection tasks,
we demonstrate: (i) the effectiveness of the learned attacking policy compared
to rule-based attacks and current end-to-end approaches; (ii) the usefulness of
the proposed credit assignment strategy and variance reduction components;
(iii) the interpretability of the policy when generating strong attacks via the
case study.
