  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.
