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：针对新闻文章的政治立场预测已被广泛研究，以缓解"回音室效应"——即人们陷入固有思维并强化既有信念。以往的政治立场研究主要聚焦于：(1)识别能反映新闻文章政治立场的政治因素，(2)有效捕捉这些因素。尽管这些方法在经验上取得了成功，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。受此启发，本研究通过用户调研探究政治立场预测的关键因素，发现新闻文章的语境与语气（隐式要素）以及文中实体涉及的外部知识（显式要素）对立场判定具有重要作用。基于这一发现，我们提出了一种新颖的知识感知政治立场预测方法（KHAN），该方法采用：(1)层次注意力网络（HAN）在三个不同层级学习词语与句子间的关联，(2)知识编码（KE）将真实世界实体的外部知识融入政治立场预测过程。此外，为捕捉对立政治立场间微妙而重要的差异，我们自主构建了两个独立的政治知识图谱（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，我们从(1)准确性、(2)效率、(3)有效性三个维度证明了DASH模型的优越性。