Contrastive Cascade Graph Learning for Classifying Real and Synthetic Information Diffusion Patterns

TL;DR

This paper evaluates Contrastive Cascade Graph Learning (CCGL) for classifying social media cascade graphs, demonstrating its effectiveness in capturing structural patterns and aiding information diffusion analysis.

Contribution

The study provides the first thorough evaluation of CCGL's performance in cascade classification, highlighting its ability to identify platform- and model-specific patterns.

Findings

CCGL effectively captures platform-specific cascade structures

CCGL outperforms baseline methods in classification accuracy

CCGL shows promise for downstream diffusion analysis tasks

Abstract

A wide variety of information is disseminated through social media, and content that spreads at scale can have tangible effects on the real world. To curb the spread of harmful content and promote the dissemination of reliable information, research on cascade graph mining has attracted increasing attention. A promising approach in this area is Contrastive Cascade Graph Learning (CCGL). One important task in cascade graph mining is cascade classification, which involves categorizing cascade graphs based on their structural characteristics. Although CCGL is expected to be effective for this task, its performance has not yet been thoroughly evaluated. This study aims to investigate the effectiveness of CCGL for cascade classification. Our findings demonstrate the strong performance of CCGL in capturing platform- and model-specific structural patterns in cascade graphs, highlighting its potential for a range of downstream information diffusion analysis tasks.