A Tutorial on Hawkes Processes for Events in Social Media
This is a tutorial for researchers or practitioners in social media analysis, presenting existing methods without new contributions.
The chapter provides an introduction to Hawkes processes for modeling interdependent events over time, focusing on social media data like retweet cascades, with results on parameter estimation and popularity prediction.
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