2019
EMNLP
EMNLP 2019
The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in Korean Media
Abstract
AbstractThis 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.
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Interdisciplinary Bridge
— Deep Learning and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— political slant classification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Deep Learning > Architectures > Transformers
Natural Language Processing > Applications > Text Classification
Interdisciplinary > Social > Social Media Analysis
Natural Language Processing > Applications > Sentiment Analysis
Machine Learning > Learning Types > Classification
Deep Learning > Models > Transformers