A Risk Communication Event Detection Model via Contrastive Learning
Mingi Shin | Sungwon Han | Sungkyu Park | Meeyoung Cha
Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
This paper presents a time-topic cohesive model describing the communication patterns on the coronavirus pandemic from three Asian countries. The strength of our model is two-fold. First, it detects contextualized events based on topical and temporal information via contrastive learning. Second, it can be applied to multiple languages, enabling a comparison of risk communication across cultures. We present a case study and discuss future implications of the proposed model.
The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in Korean Media
Jiyoung Han | Youngin Lee | Junbum Lee | Meeyoung Cha
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
This study analyzes the political slants of user comments on Korean partisan media. We built a BERT-based classifier to detect political leaning of short comments via the use of semi-unsupervised deep learning methods that produced an F1 score of 0.83. As a result of classifying 21.6K comments, we found the high presence of conservative bias on both conservative and liberal news outlets. Moreover, this study discloses an asymmetry across the partisan spectrum in that more liberals (48.0%) than conservatives (23.6%) comment not only on news stories resonating with their political perspectives but also on those challenging their viewpoints. These findings advance the current understanding of online echo chambers.
- Mingi Shin 1
- Sungwon Han 1
- Sungkyu Park 1
- Jiyoung Han 1
- Youngin Lee 1
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