Modelling of trends in Twitter using retweet graph dynamics

TL;DR

This paper models Twitter retweet graph dynamics to understand how trending topics emerge, analyzing datasets to identify key structural features and developing a mathematical model to simulate retweet behavior.

Contribution

It introduces a novel mathematical model of retweet graph evolution based on empirical analysis, capturing key features of trending topic formation.

Findings

Retweet graphs for trending topics have dense largest connected components.

Model parameters significantly influence graph density and connectivity.

The proposed model accurately replicates observed retweet graph behaviors.

Abstract

In this paper we model user behaviour in Twitter to capture the emergence of trending topics. For this purpose, we first extensively analyse tweet datasets of several different events. In particular, for these datasets, we construct and investigate the retweet graphs. We find that the retweet graph for a trending topic has a relatively dense largest connected component (LCC). Next, based on the insights obtained from the analyses of the datasets, we design a mathematical model that describes the evolution of a retweet graph by three main parameters. We then quantify, analytically and by simulation, the influence of the model parameters on the basic characteristics of the retweet graph, such as the density of edges and the size and density of the LCC. Finally, we put the model in practice, estimate its parameters and compare the resulting behavior of the model to our datasets.