  Information diffusion prediction aims at predicting the target users in the
information diffusion path on social networks. Prior works mainly focus on the
observed structure or sequence of cascades, trying to predict to whom this
cascade will be infected passively. In this study, we argue that user intent
understanding is also a key part of information diffusion prediction. We
thereby propose a novel Multi-scale Context-enhanced Dynamic Attention Network
(MCDAN) to predict which user will most likely join the observed current
cascades. Specifically, to consider the global interactive relationship among
users, we take full advantage of user friendships and global cascading
relationships, which are extracted from the social network and historical
cascades, respectively. To refine the model's ability to understand the user's
preference for the current cascade, we propose a multi-scale sequential
hypergraph attention module to capture the dynamic preference of users at
different time scales. Moreover, we design a contextual attention enhancement
module to strengthen the interaction of user representations within the current
cascade. Finally, to engage the user's own susceptibility, we construct a
susceptibility label for each user based on user susceptibility analysis and
use the rank of this label for auxiliary prediction. We conduct experiments
over four widely used datasets and show that MCDAN significantly overperforms
the state-of-the-art models. The average improvements are up to 10.61% in terms
of Hits@100 and 9.71% in terms of MAP@100, respectively.
