Predicting Information Pathways Across Online Communities
Abstract
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.
Cite
@article{arxiv.2306.02259,
title = {Predicting Information Pathways Across Online Communities},
author = {Yiqiao Jin and Yeon-Chang Lee and Kartik Sharma and Meng Ye and Karan Sikka and Ajay Divakaran and Srijan Kumar},
journal= {arXiv preprint arXiv:2306.02259},
year = {2023}
}
Comments
In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23)