Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

TL;DR

This paper introduces VAM, a hybrid simulation model combining XGBoost and probabilistic methods to forecast Twitter activity and user interactions related to economic topics, outperforming baseline models.

Contribution

The paper presents a novel hybrid simulation framework, VAM, integrating machine learning and probabilistic models for accurate Twitter activity and user link predictions.

Findings

VAM outperforms baseline models in activity forecasting

VAM accurately predicts user engagement patterns

The model effectively simulates future Twitter interactions

Abstract

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