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.

翻译：新闻文章的政治立场预测被广泛研究，以缓解“回音室效应”——即人们固守自身观点并强化既有信念。以往关于政治立场预测的研究主要聚焦于：(1) 识别能够反映新闻文章政治立场的政治因素；(2) 有效捕捉这些因素。尽管这些方法在实证上取得了成功，但其识别的因素在政治立场预测中的有效性尚未得到充分论证。为此，本文通过用户研究探究政治立场预测的关键因素，发现新闻文章的上下文与语气（隐含因素）以及文中实体相关的外部知识（显性因素）对立场判定至关重要。基于此观察，我们提出一种新颖的知识感知政治立场预测方法（KHAN），采用(1)层次注意力网络（HAN）在三个不同层级学习词与句子的关联，以及(2)知识编码（KE）将外部实体知识融入预测过程。此外，为捕捉对立政治立场间的细微重要差异，我们自主构建了两个独立的政治知识图谱（KG）（即KG-lib与KG-con），并学习融合不同政治知识。通过在三个真实数据集上的广泛评估，我们验证了KHAN在(1)准确性、(2)效率与(3)有效性方面的优越性。