Modeling the Infectiousness of Twitter Hashtags

TL;DR

This paper models the spread of Twitter hashtags using epidemic dynamics and Bayesian methods to quantify their popularity and infectiousness, providing a novel approach to understanding social media trends.

Contribution

It introduces a new application of epidemic modeling and Bayesian MCMC techniques to analyze Twitter hashtag proliferation and infectiousness.

Findings

Hashtags can be grouped by infectiousness levels.

The model estimates rates of infection and recovery for trending hashtags.

Potential to quantify trendiness of topics using epidemic parameters.

Abstract

This study applies dynamical and statistical modeling techniques to quantify the proliferation and popularity of trending hashtags on Twitter. Using time-series data reflecting actual tweets in New York City and San Francisco, we present estimates for the dynamics (i.e., rates of infection and recovery) of several hundred trending hashtags using an epidemic modeling framework coupled with Bayesian Markov Chain Monte Carlo (MCMC) methods. This methodological strategy is an extension of techniques traditionally used to model the spread of infectious disease. We demonstrate that in some models, hashtags can be grouped by infectiousness, possibly providing a method for quantifying the trendiness of a topic.