Papers by Jiyoung Han
Journalism-Guided Agentic In-context Learning for News Stance Detection (2025.emnlp-main)
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| Challenge: | Existing stance detection research on news content is limited to short texts and high-resource languages. |
| Approach: | They propose a dataset for article-level stance detection that integrates viewpoints into recommendation algorithms and a framework that employs a language model agent to predict the stances of key structural segments. |
| Outcome: | The proposed framework outperforms existing methods in identifying article stances and uncovering patterns of media bias. |
Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy (2023.findings-emnlp)
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| Challenge: | a new method to detect political bias in news articles overcomes this domain dependency . partisan bias exists in various social issues, including the 2016 presidential election . |
| Approach: | They propose a multi-head hierarchical attention model that encodes the structure of long documents through a diverse ensemble of attention heads. |
| Outcome: | The proposed model outperforms existing methods for detecting political bias in news articles. |
The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in Korean Media (D19-55)
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| Challenge: | a new study analyzes the political slants of user comments on partisan media in Korea . the classifiers detect political leaning on conservative and liberal news outlets . |
| Approach: | They built a BERT-based classifier to detect political leaning of short comments . they found a high presence of conservative bias on conservative and liberal news outlets . |
| Outcome: | The proposed classifier produced an F1 score of 0.83 for 21.6K comments . it shows that more liberals comment on stories resonating with their political perspectives . |
Detecting Contextomized Quotes in News Headlines by Contrastive Learning (2023.findings-eacl)
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| Challenge: | Existing research has found a link between the use of direct quotations and fake news. |
| Approach: | They propose a contrastive learning framework that allows embedding news quotes based on domain-driven positive and negative samples to identify such an editorial strategy. |
| Outcome: | The proposed framework maximizes the semantic similarity between the headline quote and the matched quote in the body text while minimizing similarity for other unmatched quotes in the same or other articles. |