Spreader Behavior Forecasting: Intent-aware Neural Processes for Intervening Misinformation

The behavior of spreaders on social media evolves continuously, driven by shifting intentions and interactions with emerging news topics. Traditional approaches have focused on identifying misinformation spreaders, but have often relied on a static ground-truth label, limiting their applicability for implementing time-sensitive platform interventions. In contrast, our work tackles spreader behavior forecasting through an account-level credit score, modeling the temporal evolution of spreader behavior to capture the intent shifts that drive misinformation spreading. To this end, we propose a novel Intent-aware Neural Processes (INP) model, which focuses on tracking the evolving intent of spreaders over time. The model leverages a state transition structure and an intent state thinning algorithm to improve latent representations, enabling more accurate predictions of future spreader behavior. Experimental results on restructured datasets demonstrate the effectiveness of INP in identifying temporal risk regions for proactive misinformation intervention.