Zaman: A Bayesian Approach for Predicting the Popularity of Tweets
FIG 7. Graphical model of the Bayesian log-normal-binomial model for the evolution of retweet graphs. Hyper-priors are omitted for simplicity. The plates denote replication over tweets x and users vxj.
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 paths are 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 and also revenue models for both individuals who “sell tweets” and for those looking to monetize their reach.
A Bayesian Approach for Predicting the Popularity of Tweets
Tauhid Zaman, Emily B. Fox, Eric T. Bradlow