Understanding and predicting the peak popularity of bursting hashtags

Abstract Bursting hashtags gain the bulk of popularity in a relatively short time and then peak. Understanding and predicting the peak popularity of bursting hashtags provide some insights for collective human dynamics and improve some applications. Based on a Twitter data set, we perform a temporal analysis, investigating the time-evolving properties of the subgraphs formed by users discussing each hashtag. We find that the tightness of subgraphs has a role in the formation of peak popularity. We further propose a model to predict the peak time and peak volume of bursting hashtags. The experimental results show our model outperforms other models.

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