Forecasting Violent Extremist Cyber-Recruitment The Internet’s increasing use as a means of communication has led to the formation of cyber-communities, which have become appealing to violent extremist (VE) groups. This article presents research on forecasting the daily level of cyber-recruitment activity of VE groups. We used a previously developed support vector machine model to identify recruitment posts within a western jihadist discussion forum. We analyzed the textual content of this dataset with latent Dirichlet allocation (LDA), and we fed these analyses into a variety of time series models to forecast cyber-recruitment activity within the forum. Quantitative evaluations showed that employing LDA-based topics as predictors within time series models reduces forecast error compared to naive (random-walk), ARIMA, and exponential smoothing baselines. To our knowledge, this is the first result reported on this forecasting task. This research could ultimately help assist with efficient allocation of intelligence analysts in response to predicted levels of cyber-recruitment activity.