Using SMOTE-based Data Augmentation for Social Media Time Series Prediction
Fred Mubang, Lawrence Hall · 2023
In the context of predicting activity on a social network, data for any individual will be limited. Also, low levels of activity for a topic of interest may make it difficult to build a strong predictive model. This work examines how augmentation by oversampling all activity data used to build a predictive model of user activity on Twitter can be used to improve the fidelity of predictions. Our features are counts of activity by deidentified users at the granularity of hours. It is shown that for some topics oversampling the data by creating new synthetic examples provides an effective way to increase the accuracy of predictions of future activity.