Improving Generalization for Multimodal Fake News Detection

Understand

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.