  Existing approaches for information cascade prediction fall into three main
categories: feature-driven methods, point process-based methods, and deep
learning-based methods. Among them, deep learning-based methods, characterized
by its superior learning and representation capabilities, mitigates the
shortcomings inherent of the other methods. However, current deep learning
methods still face several persistent challenges. In particular, accurate
representation of user attributes remains problematic due to factors such as
fake followers and complex network configurations. Previous algorithms that
focus on the sequential order of user activations often neglect the rich
insights offered by activation timing. Furthermore, these techniques often fail
to holistically integrate temporal and structural aspects, thus missing the
nuanced propagation trends inherent in information cascades.To address these
issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF),
which is tailored for information cascade prediction. This framework exploits
multi-hop neighborhood information to make user embeddings robust. When
embedding cascades, the framework intentionally incorporates timestamps,
endowing it with the ability to capture evolving patterns of information
diffusion. In particular, the CasCIFF seamlessly integrates the tasks of user
classification and cascade prediction into a consolidated framework, thereby
allowing the extraction of common features that prove useful for all tasks, a
strategy anchored in the principles of multi-task learning.
