The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 12, Number 4 (2018), 2175-2196.
Marked self-exciting point process modelling of information diffusion on Twitter
Information diffusion occurs on microblogging platforms like Twitter as retweet cascades. When a tweet is posted, it may be retweeted and henceforth further retweeted, and the retweeting process continues iteratively and indefinitely. A natural measure of the popularity of a tweet is the number of retweets it generates. Accurate predictions of tweet popularity can assist Twitter to rank contents more effectively and facilitate the assessment of potential for marketing and campaigning strategies. In this paper, we propose a model called the Marked Self-Exciting Process with Time-Dependent Excitation Function, or MaSEPTiDE for short, to model the retweeting dynamics and to predict the tweet popularity. Our model does not require expensive feature engineering but is capable of leveraging the observed dynamics to accurately predict the future evolution of retweet cascades. We apply our proposed methodology on a large amount of Twitter data and report substantial improvement in prediction performance over existing approaches in the literature.
Ann. Appl. Stat., Volume 12, Number 4 (2018), 2175-2196.
Received: August 2017
Revised: February 2018
First available in Project Euclid: 13 November 2018
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Chen, Feng; Tan, Wai Hong. Marked self-exciting point process modelling of information diffusion on Twitter. Ann. Appl. Stat. 12 (2018), no. 4, 2175--2196. doi:10.1214/18-AOAS1148. https://projecteuclid.org/euclid.aoas/1542078041