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.

Highlights

  • As massive amounts of online information are constantly being produced by social media sites, people are inundated with overloaded information

  • Long Short-Term Memory (LSTM) and DeepWalk [38] are adopted for social information representation, hashtag string representation, and topological network representation

  • PEAK TIME PREDICTION we present how to use multi-modal deep learning to make peak time predictions for Twitter hashtags

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Summary

INTRODUCTION

As massive amounts of online information are constantly being produced by social media sites, people are inundated with overloaded information. The problem is considered from the ‘‘time’’ angle: predicting the peak time of popularity evolution by taking Twitter hashtags as a case study. A multi-modal [33] deep learning solution is designed for predicting the peak time of popularity evolution. In this solution, LSTM and DeepWalk [38] are adopted for social information representation, hashtag string representation, and topological network representation. Zhang et al [18] predicted the popularity of social images by fusing visual features, textual features, and social features with VGGNet, LSTM, and the attention mechanism Most of these deep multi-modal papers made modalities for image features, text features and social information features. We will present how to make peak time predictions for Twitter hashtags

PEAK TIME PREDICTION
MAKE EMBEDDINGS
PREDICTION EVALUATION
CONCLUSIONS AND FUTURE WORK

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