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.

本文介绍了一种新的基于 Hawkes 过程及自激点过程的混合算法，用于预测社交媒体信息的流行度，并通过实验证明了该方法相较于目前流行的特征驱动和生成模型具有更好的性能。同时，本文通过统计用户特征和事件时间摘要数据证明了该方法在分类和回归任务中表现出色，并建议后续会有更多工作将会在特征驱动和生成建模的方法上进行比较。