고윤용/류성은/한소은’s paper has been accepted in

Title: KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
Author: Yunyong Ko, Seongeun Ryu, Soeun Han, Youngseung Jeon, Jaehoon Kim, Sohyun Park, Kyungsik Han, Hanghang Tong and Sang-Wook Kim
Abstract
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 prediction 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 accurate political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words/sentences in different levels and (2) knowledge encoding (KE) to incorporate external knowledge (both common and political) for real-world entities into the process of political stance prediction. Also, to take into account the subtle difference between opposite political stances, we build two independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) and learn to fuse the different political knowledge. Through extensive evaluations on three realworld datasets, we demonstrate the superiority of KHAN in terms of (1) accuracy, (2) efficiency, and (3) effectiveness. For the detailed information about KHAN, we have released the code of KHAN and the datasets at: https://anonymous.4open.science/r/khan.