  The problem of community-level information pathway prediction (CLIPP) aims at
predicting the transmission trajectory of content across online communities. A
successful solution to CLIPP holds significance as it facilitates the
distribution of valuable information to a larger audience and prevents the
proliferation of misinformation. Notably, solving CLIPP is non-trivial as
inter-community relationships and influence are unknown, information spread is
multi-modal, and new content and new communities appear over time. In this
work, we address CLIPP by collecting large-scale, multi-modal datasets to
examine the diffusion of online YouTube videos on Reddit. We analyze these
datasets to construct community influence graphs (CIGs) and develop a novel
dynamic graph framework, INPAC (Information Pathway Across Online Communities),
which incorporates CIGs to capture the temporal variability and multi-modal
nature of video propagation across communities. Experimental results in both
warm-start and cold-start scenarios show that INPAC outperforms seven baselines
in CLIPP.
