Rhythms in Twitter

We have examined a Twitter data set, focusing on temporal patterns observed in users' tweets and in the conversations formed by interacting users - rather than a network described by follows relations, or aggregate patterns. We have found the bursty behaviour predicted by Barabasi, but with complex patterns to the bursts. By using a clustering algorithm to group intervals between tweets, we have found that conversations show a different pattern of inter-tweet intervals to individuals, tending to: have a higher volume of quick replies, take shorter breaks, and that the timing is more variable.