Cascade size prediction in online social networks

Cascades represent an important phenomenon across various disciplines such as sociology, economy, psychology, political science, marketing, and epidemiology. The goal of this paper is to develop a model for cascade size prediction in online social networks. Specifically, given the first n edges in a cascade, we want to predict whether the cascade will have a total of at least t2 (t2 > t1) edges over its lifetime without any a priori information. In this paper, we propose a Multi-order Markov Model (M3) for cascade size prediction in online social networks. Our evaluations using a Twitter data set show that M3 based cascade size prediction scheme outperforms the baseline scheme based on cascade graph features such as edge growth rate, degree distribution, clustering, and diameter. M3 based cascade size prediction scheme consistently achieves more than 90% prediction accuracy in different experimental scenarios.

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