# Pseudo-labelling Enhanced Media Bias Detection

arXiv: https://arxiv.org/abs/2107.07705 · HTML: https://mlsanity.com/paper/2107.07705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)

## Abstract (arXiv)

Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models. This paper proposes a simple but effective data augmentation method, which leverages the idea of pseudo-labelling to select samples from noisy distant supervision annotation datasets. The result shows that the proposed method improves the accuracy of biased news detection models.