Problematic News Topic Spreader Prediction via Uncertainty-based Contrastive Learning for Marked Temporal Point Processes

Fact-checking websites try to combat rumors but can't always keep up with breaking news. Existing spreader detection methods rely on fact-checking websites to label the user's historical activities. However, these methods overlook the context of news topics and fail to predict user activities with unchecked breaking news topics, called problematic news topics in previous literature. We propose a problematic news topics spreader prediction task to bridge this gap. Methodologically, we model the prediction task with Marked Temporal Point Processes (MTPP) based on the deep learning method. Moreover, we introduce a novel hierarchical contrastive learning approach incorporating uncertainty estimation to mitigate the impact of noise prevalent in real-world social media data. This integration facilitates improved data augmentation and sequence-level sampling methods. Our approach, dubbed Uncertainty-based Contrastive Learning for MTPP (UCL-MTPP), offers both high fidelity and variety in evaluation.