Predicting Information Pathways Across Online Communities

2023/06/04 by Yiqiao Jin, Yeon-Chang Lee, Kartik Sharma +4 · 1 voice · 1 citation
Computer Science · Physics and Astronomy · Social Sciences · #Caching and Content Delivery #Complex Network Analysis Techniques #Computer science #Construct (python library) #Data mining #Data science #Graph #Information retrieval #Misinformation and Its Impacts #Modal #Online community #Theoretical computer science #World Wide Web #cs.CY #cs.SI

paper · pdf · doi:10.1145/3580305.3599470

arxiv published 2023/06/04 · arxiv updated 2023/06/04 · openalex publication_date 2023/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

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 misinfor- mation. 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 frame- work, INPAC (Information Pathway Across Online Communities), which incorporates CIGs to capture the temporal variability and multi-modal nature of video propagation across communities. Ex- perimental results in both warm-start and cold-start scenarios show that INPAC outperforms seven baselines in CLIPP. Our code and datasets are available at https://github.com/claws-lab/INPAC

Citations

• Adam: A Method for Stochastic Optimization
• The anatomy of a large-scale hypertextual Web search engine
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• The MovieLens Datasets
• Community Interaction and Conflict on the Web
• The spread of true and false news online
• Session-Based Recommendation with Graph Neural Networks
• Neural Graph Collaborative Filtering
• Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
• Neural Attentive Session-based Recommendation
• Joint Neural Collaborative Filtering for Recommender Systems
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
• LightGCN
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Conformer: Convolution-augmented Transformer for Speech Recognition
• Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
• Towards Fine-Grained Reasoning for Fake News Detection
• Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis
• SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
• Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
• Whole Page Unbiased Learning to Rank
• A Survey on Incremental Update for Neural Recommender Systems
• Deep Temporal Graph Clustering

Cited by

• CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs

Discussions

• Predicting Information Pathways Across Online Communities [hn, 2 points, 0 comments]

Related

• Predicting the popularity of information on social platforms without underlying network structure
• MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
• Structure and Dynamics of Information Pathways in Online Media
• Characterizing Information Spreading in Online Social Networks
• Viral content propagation in Online Social Networks