  Political polarization in the US is on the rise. This polarization negatively
affects the public sphere by contributing to the creation of ideological echo
chambers. In this paper, we focus on addressing one of the factors that
contributes to this polarity, polarized media. We introduce a framework for
depolarizing news articles. Given an article on a certain topic with a
particular ideological slant (eg., liberal or conservative), the framework
first detects polar language in the article and then generates a new article
with the polar language replaced with neutral expressions. To detect polar
words, we train a multi-attribute-aware word embedding model that is aware of
ideology and topics on 360k full-length media articles. Then, for text
generation, we propose a new algorithm called Text Annealing Depolarization
Algorithm (TADA). TADA retrieves neutral expressions from the word embedding
model that not only decrease ideological polarity but also preserve the
original argument of the text, while maintaining grammatical correctness. We
evaluate our framework by comparing the depolarized output of our model in two
modes, fully-automatic and semi-automatic, on 99 stories spanning 11 topics.
Based on feedback from 161 human testers, our framework successfully
depolarized 90.1% of paragraphs in semi-automatic mode and 78.3% of paragraphs
in fully-automatic mode. Furthermore, 81.2% of the testers agree that the
non-polar content information is well-preserved and 79% agree that
depolarization does not harm semantic correctness when they compare the
original text and the depolarized text. Our work shows that data-driven methods
can help to locate political polarity and aid in the depolarization of
articles.
