  Graph Neural Network (GNN)-based fake news detectors apply various methods to
construct graphs, aiming to learn distinctive news embeddings for
classification. Since the construction details are unknown for attackers in a
black-box scenario, it is unrealistic to conduct the classical adversarial
attacks that require a specific adjacency matrix. In this paper, we propose the
first general black-box adversarial attack framework, i.e., General Attack via
Fake Social Interaction (GAFSI), against detectors based on different graph
structures. Specifically, as sharing is an important social interaction for
GNN-based fake news detectors to construct the graph, we simulate sharing
behaviors to fool the detectors. Firstly, we propose a fraudster selection
module to select engaged users leveraging local and global information. In
addition, a post injection module guides the selected users to create shared
relations by sending posts. The sharing records will be added to the social
context, leading to a general attack against different detectors. Experimental
results on empirical datasets demonstrate the effectiveness of GAFSI.
