论文标题
使用XGBoost和概率混合模型模拟用户级的Twitter活动
Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models
论文作者
论文摘要
使用量 - 匹配模拟器或VAM来预测与国际经济事务相关的Twitter上的未来活动。将VAM应用于进行时间表预测，以预测：（1）总活动数量，（2）活跃的旧用户数量和（3）自预测开始24小时内新活跃用户的数量。然后，VAM使用这些卷预测来执行用户链接预测。将来的24个时间段中的每个活动分配了用户用户边缘。在时间序列和用户分配任务中，VAM的表现大大优于一组基线模型
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
