From Fake to Hyperpartisan News Detection Using Domain Adaptation
This work addresses domain adaptation for text classification in news detection, but it is incremental as it applies existing techniques to a specific domain.
The paper tackled the problem of transferring knowledge from fake to hyperpartisan news detection using unsupervised domain adaptation techniques, resulting in improved performance with further enhancements from data augmentation and clustering methods.
Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup.