The nature and origin of 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.
Keywords
Cite
@article{arxiv.1703.05545,
title = {The nature and origin of heavy tails in retweet activity},
author = {Peter Mathews and Lewis Mitchell and Giang T. Nguyen and Nigel G. Bean},
journal= {arXiv preprint arXiv:1703.05545},
year = {2018}
}
Comments
To appear in MSM 2017: 8th International Workshop on Modelling Social Media: Machine Learning and AI for Modelling and Analysing Social Media, April 2017, Perth, Australia