Lurker: Backdoor attack-based explainable rumor detection on online media.

Because of their proficiency in capturing the category characteristics of graphs, graph neural networks have shown remarkable advantages for graph-level classification tasks, that is, rumor detection and anomaly detection. Due to the manipulation of special means (e.g. bots) on online media, rumors may spread across the whole network at an overwhelming speed. Compared with normal information, popular rumors usually have a special propagation structure, especially in the early stage of information dissemination. More specifically, the special propagation structure determines whether rumors can be spread successfully. Namely, online users and their interaction that constitute the special propagation structure play a key role in the spread of rumors. Therefore, the problem of rumor detection can be transformed into detecting the existence of a special propagation structure. Inspired by backdoor attacks, we propose an interpretable rumor detection algorithm based on backdoor. Firstly, based on causal analysis, the causal sub-graph that determines the category of the graph (rumor vs. normal information) is obtained, that is, the critical online users in the rumor spreading effect are found, and then the specific propagation structure is explored. Finally, the special propagation structure is planted into the rumor detection model as a trigger. Experimental results and performance analysis on three real-world datasets demonstrate the effectiveness of our proposed algorithm in the special propagation structure detection of rumors. Compared with two baselines, Lurker achieves up to 33.1% and 61.8% performance improvement in terms of attack success rate and clean accuracy drop.