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.

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

References in corpus (3)

• Power-law distributions in empirical data
• Robust dynamic classes revealed by measuring the response function of a social system
• Parameter estimation for power-law distributions by maximum likelihood methods

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• Information flow estimation: a study of news on Twitter
• Degree distributions in networks: beyond the power law