Predicting Information Pathways Across Online Communities

• URL: http://arxiv.org/abs/2306.02259v1
• Date: Sun, 4 Jun 2023 04:41:02 GMT
• Title: Predicting Information Pathways Across Online Communities
• Authors: Yiqiao Jin, Yeon-Chang Lee, Kartik Sharma, Meng Ye, Karan Sikka, Ajay Divakaran, Srijan Kumar
• Abstract summary: The problem of community-level information pathway prediction (CLIPP) aims at predicting the transmission trajectory of content across online communities. We analyze large-scale, multi-modal datasets to examine the diffusion of online YouTube videos on Reddit. We develop a novel dynamic graph framework, INPAC, which incorporates CIGs to capture the temporal variability and multi-modal nature of video propagation.
• Score: 23.48675035152965
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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