  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.
