The Annals of Applied Statistics

A Bayesian approach for predicting the popularity of tweets

Tauhid Zaman, Emily B. Fox, and Eric T. Bradlow

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Abstract

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.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 3 (2014), 1583-1611.

Dates
First available in Project Euclid: 23 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1414091226

Digital Object Identifier
doi:10.1214/14-AOAS741

Mathematical Reviews number (MathSciNet)
MR3271345

Zentralblatt MATH identifier
1303.62048

Keywords
Social networks Twitter Bayesian inference time series forecasting

Citation

Zaman, Tauhid; Fox, Emily B.; Bradlow, Eric T. A Bayesian approach for predicting the popularity of tweets. Ann. Appl. Stat. 8 (2014), no. 3, 1583--1611. doi:10.1214/14-AOAS741. https://projecteuclid.org/euclid.aoas/1414091226


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Supplemental materials

  • Supplementary material: Retweet time series data. These files contain the data of the retweet time series for the root tweets studied in this paper. They also include the files which contain the different partitions of the tweets into training and prediction sets used for the analysis in this paper.
    Digital Object Identifier: doi:10.1214/14-AOAS741SUPP
    Supplemental ZIP File (406 KB)

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