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There is considerable interest in developing techniques for predicting human behavior, and a promising approach to this problem is to collect phenomenon-relevant empirical data and then apply machine learning methods to these data to form predictions. This two-part paper shows that the performance of such learning algorithms often can be improved substantially by leveraging sociological models in their development and implementation. In this paper, the second of the two parts, we demonstrate that a sociologically-grounded learning algorithm outperforms a gold-standard method for the task of predicting whether nascent social diffusion events will “go viral”. Significantly, the proposed algorithm performs well even when there is only limited time series data available for analysis.