VAM: An End-to-End Simulator for Time Series Regression and Temporal Link Prediction in Social Media Networks

Abstract

We present a machine-learning-driven end-to-end simulator, called the <i>Volume-Audience-Match</i> (VAM) simulator. VAM&#x2019;s purpose is to simulate future phenomena related to various topics of discussion in social media networks. We focus our attention on the social media platform, Twitter, due to its abundant use in today&#x2019;s world. VAM was applied to do time series forecasting to predict the future: 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 macroscopic volume predictions (VPs) to perform user link predictions. A user&#x2013;user edge was assigned to each of the activities in the 24 future time steps. We report that VAM outperformed multiple baseline models in the time series task, which were the auto-regressive integrated moving average (ARIMA), auto regressive moving average (ARMA), auto regressive (AR), moving average (MA), Persistence Baseline, and state-of-the-art tNodeEmbed models. Furthermore, we show that VAM outperformed the Persistence Baseline and tNodeEmbed models used for the user-assignment tasks. Finally, it is also shown that using Reddit activity data improves prediction accuracy.