Prediction of retweet cascade size over time

  • Authors:

  • Andrey Kupavskii;Liudmila Ostroumova;Alexey Umnov;Svyatoslav Usachev;Pavel Serdyukov;Gleb Gusev;Andrey Kustarev

  • Affiliations:

  • Yandex Corporate, Moscow, Russian Fed.;Yandex Corporate, Moscow, Russian Fed.;Yandex Corporate, Moscow, Russian Fed.;Yandex Corporate, Moscow, Russian Fed.;Yandex Corporate, Moscow, Russian Fed.;Yandex Corporate, Moscow, Russian Fed.;Yandex Corporate, Moscow, Russian Fed.

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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Abstract

Retweet cascades play an essential role in information diffusion in Twitter. Popular tweets reflect the current trends in Twitter, while Twitter itself is one of the most important online media. Thus, understanding the reasons why a tweet becomes popular is of great interest for sociologists, marketers and social media researches. What is even more important is the possibility to make a prognosis of a tweet's future popularity. Besides the scientific significance of such possibility, this sort of prediction has lots of practical applications such as breaking news detection, viral marketing etc. In this paper we try to forecast how many retweets a given tweet will gain during a fixed time period. We train an algorithm that predicts the number of retweets during time T since the initial moment. In addition to a standard set of features we utilize several new ones. One of the most important features is the flow of the cascade. Another one is PageRank on the retweet graph, which can be considered as the measure of influence of users.