  Most previous analysis of Twitter user behavior is focused on individual
information cascades and the social followers graph. We instead study aggregate
user behavior and the retweet graph with a focus on quantitative descriptions.
We find that the lifetime tweet distribution is a type-II discrete Weibull
stemming from a power law hazard function, the tweet rate distribution,
although asymptotically power law, exhibits a lognormal cutoff over finite
sample intervals, and the inter-tweet interval distribution is power law with
exponential cutoff. The retweet graph is small-world and scale-free, like the
social graph, but is less disassortative and has much stronger clustering.
These differences are consistent with it better capturing the real-world social
relationships of and trust between users. Beyond just understanding and
modeling human communication patterns and social networks, applications for
alternative, decentralized microblogging systems-both predicting real-word
performance and detecting spam-are discussed.
