[WWW'23] KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
This repository provides an implementation of KHAN as described in the paper: KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction by Yunyong Ko, Seongeun Ryu, Soeun Han, Youngseung Jeon, Jaehoon Kim, Sohyun Park, Kyungsik Han, Hanghang Tong, and Sang-Wook Kim, In Proceedings of the ACM Web Conference (WWW) 2023.
- Datasets
- To reflect the different political knowledge of each entity, we build two political knowledge graphs, KG-lib and KG-con. Also, for extensive evaluation, we construct a large-scale political news datatset, AllSides-L, much larger (48X) than the existing largest political news article dataset.
- Algorithm
- We propose a novel approach to accurate political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) and (2) knowledge encoding (KE) to effectively capture both explicit and implicit factors of a news article.
- Evaluation
- Via extensive experiments, we demonstrate that (1) (accuracy) KHAN consistently achieves higher accuracies than all competing methods (up to 5.92% higher than the state-of-the-art method), (2) (efficiency) KHAN converges within comparable training time/epochs, and (3) (effectiveness) each of the main components of KHAN is effective in political stance prediction.
- News articles datasets (SemEval, AllSides-S, AllSides-L)
| Dataset | # of articles | Class distribution |
|---|---|---|
| SemEval | 645 | 407 / 238 |
| AllSides-S | 14.7k | 6.6k / 4.6k / 3.5k |
| AllSides-L | 719.2k | 112.4k / 202.9k / 99.6k / 62.6k / 241.5k |
- Knowledge Graphs (YAGO, KG-conservative, KG-liberal)
| KG dataset | # of source poses | # of entities | # of raltions |
|---|---|---|---|
| YAGO | - | 123,182 | 1,179,040 |
| KG-lib | 219,915 | 5,581 | 29,967 |
| KG-con | 276,156 | 6,316 | 33,207 |
- Pre-trained KG embeddings (common, conservative, liberal)
Our code runs on the Intel i7-9700k CPU with 64GB memory and NVIDIA RTX 2080 Ti GPU with 12GB, with the following packages installed:
python 3.8.10
torch 1.11.0
torchtext 0.12.0
pandas
numpy
argparse
sklearn
python3 main.py \
--gpu_index=0 \
--batch_size=16 \
--num_epochs=50 \
--learning_rate=0.001 \
--max_sentence=20 \
--embed_size=256 \
--dropout=0.3 \
--num_layer=1 \
--num_head=4 \
--d_hid=128 \
--dataset=SEMEVAL \
--alpha=0.6 \
--beta=0.2
Please cite our paper if you have used the code in your work. You can use the following BibTex citation:
@inproceedings{ko2023khan,
title={KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction},
author={Ko, Yunyong and Ryu, Seongeun and Han, Soeun and Jeon,Youngseung and Kim, Jaehoon and Park, Sohyun and Han, Kyungsik Tong, Hanghang and Kim., Sang-Wook},
booktitle={Proceedings of the ACM Web Conference (WWW) 2023},
pages={1572--1583},
year={2023},
isbn = {9781450394161},
publisher = {Association for Computing Machinery (ACM)},
doi = {10.1145/3543507.3583300},
location = {Austin, TX, USA},
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent