Target-Aware Contextual Political Bias Detection in News
Iffat Maab Edison Marrese-Taylor Yutaka Matsuo
Media bias detection requires comprehensive integration of information
derived from multiple news sources. Sentence-level political bias detection in
news is no exception, and has proven to be a challenging task that requires an
understanding of bias in consideration of the context. Inspired by the fact
that humans exhibit varying degrees of writing styles, resulting in a diverse
range of statements with different local and global contexts, previous work in
media bias detection has proposed augmentation techniques to exploit this fact.
Despite their success, we observe that these techniques introduce noise by
over-generalizing bias context boundaries, which hinders performance. To
alleviate this issue, we propose techniques to more carefully search for
context using a bias-sensitive, target-aware approach for data augmentation.
Comprehensive experiments on the well-known BASIL dataset show that when
combined with pre-trained models such as BERT, our augmentation techniques lead
to state-of-the-art results. Our approach outperforms previous methods
significantly, obtaining an F1-score of 58.15 over state-of-the-art bias
detection task.