Predicting Retweet Scale Using Log-Normal Distribution

In social network analysis, retweet scale prediction is one important studying focus. Generally speaking, there are two different approaches to predict the retweet scale: time-series approach and non-time-series approach. In this paper, we conduct a research on the distribution of the reaction time in retweeting activity and introduce a time-series prediction model. We show that in retweeting activity, the reaction time has the feature of heavy-tailed distribution and the log-normal distribution fits the real reaction time data well. Within the framework of time-series prediction, for the direct retweets, we make the prediction by solving the parameter estimation problem of truncated log-normal distribution. For retweets at deeper depths, we make a prediction based on the general information diffusion theory. Experiments are carried out on real data downloaded from SINA weibo. We test the full model on retweet graphs and compare our model with the auto regression model and a perceptron model using tweet text. Our method outperforms the other two models and in experiment, on average, there is a 2% advantage over the auto-regression model when one-hour data are given.