  Toxic comment classification models are often found biased toward identity
terms which are terms characterizing a specific group of people such as
"Muslim" and "black". Such bias is commonly reflected in false-positive
predictions, i.e. non-toxic comments with identity terms. In this work, we
propose a novel approach to tackle such bias in toxic comment classification,
leveraging the notion of subjectivity level of a comment and the presence of
identity terms. We hypothesize that when a comment is made about a group of
people that is characterized by an identity term, the likelihood of that
comment being toxic is associated with the subjectivity level of the comment,
i.e. the extent to which the comment conveys personal feelings and opinions.
Building upon the BERT model, we propose a new structure that is able to
leverage these features, and thoroughly evaluate our model on 4 datasets of
varying sizes and representing different social media platforms. The results
show that our model can consistently outperform BERT and a SOTA model devised
to address identity term bias in a different way, with a maximum improvement in
F1 of 2.43% and 1.91% respectively.
