Improving Generalization for Multimodal Fake News Detection
This addresses the issue of misinformation detection for social media applications, but it is incremental as it builds on existing methods.
The paper tackled the problem of multimodal fake news detection by proposing models that fine-tune state-of-the-art multimodal transformers, and the result showed that data augmentation techniques improved generalization and achieved state-of-the-art results.
The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for fake news detection. However, state-of-the-art approaches are usually trained on datasets of smaller size or with a limited set of specific topics. As a consequence, these models lack generalization capabilities and are not applicable to real-world data. In this paper, we propose three models that adopt and fine-tune state-of-the-art multimodal transformers for multimodal fake news detection. We conduct an in-depth analysis by manipulating the input data aimed to explore models performance in realistic use cases on social media. Our study across multiple models demonstrates that these systems suffer significant performance drops against manipulated data. To reduce the bias and improve model generalization, we suggest training data augmentation to conduct more meaningful experiments for fake news detection on social media. The proposed data augmentation techniques enable models to generalize better and yield improved state-of-the-art results.