  We predict the popularity of short messages called tweets created in the
micro-blogging site known as Twitter. We measure the popularity of a tweet by
the time-series path of its retweets, which is when people forward the tweet to
others. We develop a probabilistic model for the evolution of the retweets
using a Bayesian approach, and form predictions using only observations on the
retweet times and the local network or "graph" structure of the retweeters. We
obtain good step ahead forecasts and predictions of the final total number of
retweets even when only a small fraction (i.e., less than one tenth) of the
retweet path is observed. This translates to good predictions within a few
minutes of a tweet being posted, and has potential implications for
understanding the spread of broader ideas, memes, or trends in social networks.
