  In the ever-evolving landscape of social network advertising, the volume and
accuracy of data play a critical role in the performance of predictive models.
However, the development of robust predictive algorithms is often hampered by
the limited size and potential bias present in real-world datasets. This study
presents and explores a generative augmentation framework of social network
advertising data. Our framework explores three generative models for data
augmentation - Generative Adversarial Networks (GANs), Variational Autoencoders
(VAEs), and Gaussian Mixture Models (GMMs) - to enrich data availability and
diversity in the context of social network advertising analytics effectiveness.
By performing synthetic extensions of the feature space, we find that through
data augmentation, the performance of various classifiers has been
quantitatively improved. Furthermore, we compare the relative performance gains
brought by each data augmentation technique, providing insights for
practitioners to select appropriate techniques to enhance model performance.
This paper contributes to the literature by showing that synthetic data
augmentation alleviates the limitations imposed by small or imbalanced datasets
in the field of social network advertising. At the same time, this article also
provides a comparative perspective on the practicality of different data
augmentation methods, thereby guiding practitioners to choose appropriate
techniques to enhance model performance.
