Open Access
September 2014 A Bayesian approach for predicting the popularity of tweets
Tauhid Zaman, Emily B. Fox, Eric T. Bradlow
Ann. Appl. Stat. 8(3): 1583-1611 (September 2014). DOI: 10.1214/14-AOAS741


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.


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Tauhid Zaman. Emily B. Fox. Eric T. Bradlow. "A Bayesian approach for predicting the popularity of tweets." Ann. Appl. Stat. 8 (3) 1583 - 1611, September 2014.


Published: September 2014
First available in Project Euclid: 23 October 2014

zbMATH: 1303.62048
MathSciNet: MR3271345
Digital Object Identifier: 10.1214/14-AOAS741

Keywords: Bayesian inference , forecasting , social networks , time series , Twitter

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 3 • September 2014
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