A Tutorial on Hawkes Processes for Events in Social Media

TL;DR

This tutorial introduces Hawkes processes for modeling social media events, explaining their concepts, simulation, and parameter estimation, with practical examples on retweet cascades and available code resources.

Contribution

It provides a comprehensive, accessible guide to applying Hawkes processes to social media data, including modeling techniques and real-world case studies.

Findings

Effective modeling of retweet cascades using Hawkes processes

Successful parameter estimation and popularity prediction

Availability of code and data for reproducibility

Abstract

This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event simulation and parameter estimation. We also describe a practical example drawn from social media data - we show how to model retweet cascades using a Hawkes self-exciting process. We presents a design of the memory kernel, and results on estimating parameters and predicting popularity. The code and sample event data are available as an online appendix