Predicting Rumor Veracity on Social Media with Graph Structured Multi-task Learning

Previous studies have shown that the multi-task learning paradigm with the stance classification could facilitate the successful detection of rumours, but the shared layers in multi-task learning tend to yield a compromise between the general and the task-specific representation of structural information. To address this issue, we propose a novel Multi-Task Learning framework with Shared Multi-channel Interactions (MTL-SMI), which is composed of two shared channels and two task-specific graph channels. The shared channels extract task-invariant text features and structural features, and the task-specific graph channels, by interacting with the shared channels, extract the task-enhanced structural features. Experiments on two realworld datasets show the superiority of MTL-SMI against strong baselines.