The political stance prediction for news articles has been widely studied to
mitigate the echo chamber effect -- people fall into their thoughts and
reinforce their pre-existing beliefs. The previous works for the political
stance problem focus on (1) identifying political factors that could reflect
the political stance of a news article and (2) capturing those factors
effectively. Despite their empirical successes, they are not sufficiently
justified in terms of how effective their identified factors are in the
political stance prediction. Motivated by this, in this work, we conduct a user
study to investigate important factors in political stance prediction, and
observe that the context and tone of a news article (implicit) and external
knowledge for real-world entities appearing in the article (explicit) are
important in determining its political stance. Based on this observation, we
propose a novel knowledge-aware approach to political stance prediction (KHAN),
employing (1) hierarchical attention networks (HAN) to learn the relationships
among words and sentences in three different levels and (2) knowledge encoding
(KE) to incorporate external knowledge for real-world entities into the process
of political stance prediction. Also, to take into account the subtle and
important difference between opposite political stances, we build two
independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by
ourselves and learn to fuse the different political knowledge. Through
extensive evaluations on three real-world datasets, we demonstrate the
superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3)
effectiveness.

本研究提出了一种基于知识感知的方法，利用分层注意力网络和知识编码技术，将新闻文章的上下文信息和实体外在知识融入到政治倾向预测的过程中，建立了两个独立的政治知识图谱进行对比，取得高精度和高效性。