Predicting Information Pathways Across Online Communities

Our Proposed INPAC Framework


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. Our code and datasets are available here

In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Yiqiao Jin
Yiqiao Jin
Graduate Research Assistant at Georgia Institute of Technology

My research interests include Computational Social Science, Misinformation, Graph Analysis, and Data Mining.