It's All Relative: Interpretable Models for Scoring Bias in Documents
Aswin Suresh Chi-Hsuan Wu Matthias Grossglauser
We propose an interpretable model to score the bias present in web documents,
based only on their textual content. Our model incorporates assumptions
reminiscent of the Bradley-Terry axioms and is trained on pairs of revisions of
the same Wikipedia article, where one version is more biased than the other.
While prior approaches based on absolute bias classification have struggled to
obtain a high accuracy for the task, we are able to develop a useful model for
scoring bias by learning to perform pairwise comparisons of bias accurately. We
show that we can interpret the parameters of the trained model to discover the
words most indicative of bias. We also apply our model in three different
settings - studying the temporal evolution of bias in Wikipedia articles,
comparing news sources based on bias, and scoring bias in law amendments. In
each case, we demonstrate that the outputs of the model can be explained and
validated, even for the two domains that are outside the training-data domain.
We also use the model to compare the general level of bias between domains,
where we see that legal texts are the least biased and news media are the most
biased, with Wikipedia articles in between. Given its high performance,
simplicity, interpretability, and wide applicability, we hope the model will be
useful for a large community, including Wikipedia and news editors, political
and social scientists, and the general public.