Mitigating Framing Bias with Polarity Minimization Loss
Yejin Bang Nayeon Lee Pascale Fung
Framing bias plays a significant role in exacerbating political polarization
by distorting the perception of actual events. Media outlets with divergent
political stances often use polarized language in their reporting of the same
event. We propose a new loss function that encourages the model to minimize the
polarity difference between the polarized input articles to reduce framing
bias. Specifically, our loss is designed to jointly optimize the model to map
polarity ends bidirectionally. Our experimental results demonstrate that
incorporating the proposed polarity minimization loss leads to a substantial
reduction in framing bias when compared to a BART-based multi-document
summarization model. Notably, we find that the effectiveness of this approach
is most pronounced when the model is trained to minimize the polarity loss
associated with informational framing bias (i.e., skewed selection of
information to report).