Let Me Generate That for You: Generative Data Augmentation for Misinformation Detection in Low-Resource Environments

Misinformation detection is a rapidly moving target, as new topics emerge and evolve in high volume on social media platforms. Annotated and fact-checked datasets are necessary for detection model training, but are laborious to curate. Thus, many misinformation detection models are trained in low-resource environments and rely on machine learning techniques to improve performance with small ground-truth datasets. Generative data augmentation methods enable topic-specific examples that increase a model's training dataset without incurring the cost and time investment associated with manual annotation. In this work, we assess the value of using generative augmentation for different classes of learning models: a classic neural model, a fine-tuned deep learning model, a reinforcement learning model, and an active learning model. We find that generated training data is not effective for all learning paradigms for the misinformation detection task, highlighting the need to use different quality measures to assess its value for low-resource machine learning tasks.