A Comparison of Methods for Cascade Prediction

TL;DR

This paper compares various methods for predicting viral information cascades on social networks, evaluating their accuracy and run time to guide system developers in choosing appropriate techniques.

Contribution

It formulates cascade prediction as classification and regression, and systematically compares centrality, feature, and point process methods across multiple metrics.

Findings

Feature based methods achieve higher accuracy but are computationally intensive.

Point process methods have longer run times when models do not adapt well.

No single method outperforms others in all aspects, highlighting trade-offs.

Abstract

Information cascades exist in a wide variety of platforms on Internet. A very important real-world problem is to identify which information cascades can go viral. A system addressing this problem can be used in a variety of applications including public health, marketing and counter-terrorism. As a cascade can be considered as compound of the social network and the time series. However, in related literature where methods for solving the cascade prediction problem were proposed, the experimental settings were often limited to only a single metric for a specific problem formulation. Moreover, little attention was paid to the run time of those methods. In this paper, we first formulate the cascade prediction problem as both classification and regression. Then we compare three categories of cascade prediction methods: centrality based, feature based and point process based. We carry out the comparison through evaluation of the methods by both accuracy metrics and run time. The results show that feature based methods can outperform others in terms of prediction accuracy but suffer from heavy overhead especially for large datasets. While point process based methods can also run into issue of long run time when the model can not well adapt to the data. This paper seeks to address issues in order to allow developers of systems for social network analysis to select the most appropriate method for predicting viral information cascades.