2020
EMNLP
EMNLP 2020
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media
Abstract
AbstractIn this paper, we suggest a minimally supervised approach for identifying nuanced frames in news article coverage of politically divisive topics. We suggest to break the broad policy frames suggested by Boydstun et al., 2014 into fine-grained subframes which can capture differences in political ideology in a better way. We evaluate the suggested subframes and their embedding, learned using minimal supervision, over three topics, namely, immigration, gun-control, and abortion. We demonstrate the ability of the subframes to capture ideological differences and analyze political discourse in news media.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
📈
Trend Setter
— Digital Humanities
🐣
Hot Topic Early Bird
— political discourse
🐝
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 > Representation Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Natural Language Processing > Understanding > Semantic Analysis
Interdisciplinary > Social > Digital Humanities
Artificial Intelligence > Core AI > Natural Language Processing
Machine Learning > Learning Paradigms > Weakly Supervised Learning