A Prediction Method of Peak Time Popularity Based on Twitter Hashtags

Abstract

Understanding the peak time of popularity evolution can provide insights on recommendation systems and online advertising campaigns. Although popularity evolution has been largely studied, the problem of how to predict its peak time remains unexplored. Taking Twitter hashtags as case study, the goal of this study is to predict when popularity reaches the peak for Twitter hashtags, from the perspective of an online social network application, in the context of the Twitter social network. On the whole, this paper includes three research aspects. Firstly, this paper investigates how early popularity reaches its peaks. Then, it is found that popularity tends to peak in the early stage of its evolution. Secondly, this paper discusses when a peak time prediction should be triggered. Thirdly, this paper designs a multi-modal based deep learning method, where the state-of-art deep learning techniques, such as multi-modal embedding and attention mechanisms, are adopted. We find that in the early stage of popularity evolution, no matter which factor is used as the input, the prediction effect is poor. By contrast, the hashtag string factor has the weakest contribution to popularity prediction in the middle and late stages of popularity evolution. The overall performance of our proposed method is evaluated in terms of the minimum, quartiles, and maximum values of absolute errors. From the experimental results, the prediction method we designed is superior.