Submitted to the Annals of Applied Statistics A BAYESIAN APPROACH FOR PREDICTING THE POPULARITY OF TWEETS

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 min-utes of a tweet being posted, and has potential implications for understanding the spread of broader ideas, memes, or trends in social networks. 1. Introduction. Th