Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models
This work addresses activity prediction for social media platforms, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of predicting future Twitter activity and user interactions related to international economic affairs, achieving significant improvements over baseline models in both time series forecasting and user-assignment tasks.
The Volume-Audience-Match simulator, or VAM was applied to predict future activity on Twitter related to international economic affairs. VAM was applied to do timeseries forecasting to predict the: (1) number of total activities, (2) number of active old users, and (3) number of newly active users over the span of 24 hours from the start time of prediction. VAM then used these volume predictions to perform user link predictions. A user-user edge was assigned to each of the activities in the 24 future timesteps. VAM considerably outperformed a set of baseline models in both the time series and user-assignment tasks