KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction

• URL: http://arxiv.org/abs/2302.12126v3
• Date: Tue, 4 Apr 2023 20:03:36 GMT
• Title: KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
• Authors: Yunyong Ko, Seongeun Ryu, Soeun Han, Youngseung Jeon, Jaehoon Kim, Sohyun Park, Kyungsik Han, Hanghang Tong, Sang-Wook Kim
• Abstract summary: We propose a novel knowledge-aware approach to political stance prediction (KHAN) We employ (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. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.
• Score: 37.261840137596195
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Biased AI can Influence Political Decision-Making [64.9461133083473]
  This paper presents two experiments investigating the effects of partisan bias in AI language models on political decision-making. We found that participants exposed to politically biased models were significantly more likely to adopt opinions and make decisions aligning with the AI's bias.
  arXiv  Detail & Related papers  (2024-10-08T22:56:00Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System [23.289938642423298]
  We present HearHere, an AI-based web system designed to help users accommodate information and opinions from diverse perspectives. Our findings highlight the importance of providing political stance information and quantifying users' political status as a means to mitigate political polarization.
  arXiv  Detail & Related papers  (2024-02-28T10:37:14Z)
• Examining Political Rhetoric with Epistemic Stance Detection [13.829628375546568]
  We develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. We demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books.
  arXiv  Detail & Related papers  (2022-12-29T23:47:14Z)
• PAR: Political Actor Representation Learning with Social Context and Expert Knowledge [45.215862050840116]
  We propose textbfPAR, a textbfPolitical textbfActor textbfRepresentation learning framework. We retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations.
  arXiv  Detail & Related papers  (2022-10-15T19:28:06Z)
• Knowledge Graph Augmented Political Perspective Detection in News Media [7.477393857078695]
  We propose a perspective detection method that incorporates external knowledge of real-world politics. Our method achieves the best performance and outperforms state-of-the-art methods by 5.49%.
  arXiv  Detail & Related papers  (2021-08-09T08:05:56Z)
• Analyzing Online Political Advertisements [10.386018392170083]
  We present the first computational study on online political ads with the aim to infer the political ideology of an ad sponsor. We develop two new large datasets for the two tasks consisting of ads from the U.S.
  arXiv  Detail & Related papers  (2021-05-09T23:18:37Z)
• Political Posters Identification with Appearance-Text Fusion [49.55696202606098]
  We propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters. The majority of this work focuses on political posters that are designed to serve as a promotion of a certain political event.
  arXiv  Detail & Related papers  (2020-12-19T16:14:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.