# The nature and origin of heavy tails in retweet activity

## TL;DR

This paper investigates the distribution of retweet times on Twitter, showing that a power law with exponential cutoff better models the data, explained by human burstiness and task prioritization.

## Contribution

It introduces a more accurate model for retweet timing distributions, incorporating burstiness and task priorities to explain heavy tails.

## Key findings

- Power law with exponential cutoff fits retweet times better than pure power laws
- Human burstiness influences retweet timing distributions
- Task prioritization explains the heavy tails in retweet activity

## Abstract

Modern social media platforms facilitate the rapid spread of information online. Modelling phenomena such as social contagion and information diffusion are contingent upon a detailed understanding of the information-sharing processes. In Twitter, an important aspect of this occurs with retweets, where users rebroadcast the tweets of other users. To improve our understanding of how these distributions arise, we analyse the distribution of retweet times. We show that a power law with exponential cutoff provides a better fit than the power laws previously suggested. We explain this fit through the burstiness of human behaviour and the priorities individuals place on different tasks.