A Bayesian approach for predicting the popularity of tweets
Abstract
We predict the popularity of short messages called tweets created in the microblogging site known as Twitter. We measure the popularity of a tweet by the timeseries 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.
 Publication:

arXiv eprints
 Pub Date:
 April 2013
 arXiv:
 arXiv:1304.6777
 Bibcode:
 2013arXiv1304.6777Z
 Keywords:

 Computer Science  Social and Information Networks;
 Physics  Physics and Society;
 Statistics  Applications
 EPrint:
 Published in at http://dx.doi.org/10.1214/14AOAS741 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)