Implementation of chaotic analysis on retweet time series

Retweet has become one of the most prominent feature on social networks and an important mean for secondary content promotion. Most existing investigations of retweet behaviors on social networks are conducted based on empirical studies or information diffusion models (such as stochastic process or cascading model). To the best of our knowledge, such a retweet process has not been investigated as a chaotic process. In this paper, we have first examined that retweet time series by 0-1 test where the results provide identification of chaotic behaviors. Furthermore, taking into account of the proven chaotic characteristic, chaos LS-SVM prediction method is applied to form predictions using only a small fraction of the retweet time series. Our evaluation on Sina Weibo dataset and comparisons with a bayesian model and strawman modal show that this nonlinear prediction method can translate to good step ahead forecasts and perform high accuracy in retweet prediction.