Feature Driven and Point Process Approaches for Popularity Prediction

TL;DR

This paper introduces a hybrid Hawkes process model for predicting social media popularity, outperforming existing methods and highlighting the value of combining feature-driven and generative approaches.

Contribution

A novel hybrid approach using marked Hawkes processes for popularity prediction, bridging feature-driven and generative models, with a new benchmark dataset and comprehensive evaluation.

Findings

Hawkes process with predictive overlay outperforms existing methods on tweet data

Basic user features and event statistics are competitive for prediction

Adding point process info improves prediction accuracy

Abstract

Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with a new hybrid approach and a new performance benchmark. We model each social cascade with a marked Hawkes self-exciting point process, and estimate the content virality, memory decay, and user influence. We then learn a predictive layer for popularity prediction using a collection of cascade history. To our surprise, Hawkes process with a predictive overlay outperform recent feature-driven and generative approaches on existing tweet data [43] and a new public benchmark on news tweets. We also found that a basic set of user features and event time summary statistics performs competitively in both classification and regression tasks, and that adding point process information to the feature set further improves predictions. From these observations, we argue that future work on popularity prediction should compare across feature-driven and generative modeling approaches in both classification and regression tasks.