  Cascade prediction estimates the size or the state of a cascade from either
microscope or macroscope. It is of paramount importance for understanding the
information diffusion process such as the spread of rumors and the propagation
of new technologies in social networks. Recently, instead of extracting
hand-crafted features or embedding cascade sequences into feature vectors for
cascade prediction, graph neural networks (GNNs) are introduced to utilize the
network structure which governs the cascade effect. However, these models do
not take into account social factors such as personality traits which drive
human's participation in the information diffusion process. In this work, we
propose a novel multitask framework for enhancing cascade prediction with a
personality recognition task. Specially, we design a general plug-and-play GNN
gate, named PersonalityGate, to couple into existing GNN-based cascade
prediction models to enhance their effectiveness and extract individuals'
personality traits jointly. Experimental results on two real-world datasets
demonstrate the effectiveness of our proposed framework in enhancing GNN-based
cascade prediction models and in predicting individuals' personality traits as
well.
