  The wide spread of rumors on social media has caused a negative impact on
people's daily life, leading to potential panic, fear, and mental health
problems for the public. How to debunk rumors as early as possible remains a
challenging problem. Existing studies mainly leverage information propagation
structure to detect rumors, while very few works focus on correlation among
users that they may coordinate to spread rumors in order to gain large
popularity. In this paper, we propose a new detection model, that jointly
learns both the representations of user correlation and information propagation
to detect rumors on social media. Specifically, we leverage graph neural
networks to learn the representations of user correlation from a bipartite
graph that describes the correlations between users and source tweets, and the
representations of information propagation with a tree structure. Then we
combine the learned representations from these two modules to classify the
rumors. Since malicious users intend to subvert our model after deployment, we
further develop a greedy attack scheme to analyze the cost of three adversarial
attacks: graph attack, comment attack, and joint attack. Evaluation results on
two public datasets illustrate that the proposed MODEL outperforms the
state-of-the-art rumor detection models. We also demonstrate our method
performs well for early rumor detection. Moreover, the proposed detection
method is more robust to adversarial attacks compared to the best existing
method. Importantly, we show that it requires a high cost for attackers to
subvert user correlation pattern, demonstrating the importance of considering
user correlation for rumor detection.
