A hierarchical model of non-homogeneous Poisson processes for Twitter retweets

TL;DR

This paper introduces a hierarchical non-homogeneous Poisson process model for Twitter retweets that accounts for time decay and follower influence, enabling prediction and model selection.

Contribution

A novel hierarchical NHPP model for Twitter retweets incorporating follower count effects and an inference algorithm for Bayesian model comparison.

Findings

Model accurately predicts retweet counts.

Incorporates follower influence into retweet dynamics.

Facilitates model selection via Bayes factor.

Abstract

We present a hierarchical model of non-homogeneous Poisson processes (NHPP) for information diffusion on online social media, in particular Twitter retweets. The retweets of each original tweet are modelled by a NHPP, for which the intensity function is a product of time-decaying components and another component that depends on the follower count of the original tweet author. The latter allows us to explain or predict the ultimate retweet count by a network centrality-related covariate. The inference algorithm enables the Bayes factor to be computed, in order to facilitate model selection. Finally, the model is applied to the retweet data sets of two hashtags.