SEISMIC: A self-exciting point process model for predicting tweet popularity


We study a simple but extremely noisy form of information cascade—tweets. We use a doubly stochastic self-exciting point process to model the retweet process. The SEISMIC model we develop only requires the timestamps and the graph degrees to make more accurate predictions than the state-of-the-art.

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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