A Stochastic Simulation of the Decision to Retweet

  • Ronald Hochreiter
  • Christoph Waldhauser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8176)


Twitter is a popular microblogging platform that sees a vast increase in use as a marketing communication tool. For any marketing campaign to be successful, word-of-mouth is an essential component. The equivalent of word-of-mouth propagation in Twitter is the retweeting of a message. So far, little focus has been put on how Twitter users arrive at deciding which tweets to retweet and which ones to ignore. This contribution offers a stochastic decision function that models a nodes decision process. This model is embedded in a simulation of an entire communication network. The contained nodes characterizations are derived from genuine Twitter data. A genetic algorithm is used to find a message that is retweeted by a maximum number of nodes. We find that the stochastic nature of the retweeting decision contributes to a large amount of uncertainty. However, the genetic algorithm is able to increase the scale on which a message is being retweeted significantly.


Twitter social network message style genetic algorithm deterministic optimization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ronald Hochreiter
    • 1
  • Christoph Waldhauser
    • 1
  1. 1.Department for Finance, Accounting and StatisticsWU Vienna University of Economics and BusinessAustria

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