Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media

Shamik Roy, Dan Goldwasser


Abstract
In 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.
Anthology ID:
2020.emnlp-main.620
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7698–7716
Language:
URL:
https://aclanthology.org/2020.emnlp-main.620
DOI:
10.18653/v1/2020.emnlp-main.620
Bibkey:
Cite (ACL):
Shamik Roy and Dan Goldwasser. 2020. Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7698–7716, Online. Association for Computational Linguistics.
Cite (Informal):
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media (Roy & Goldwasser, EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.620.pdf
Optional supplementary material:
 2020.emnlp-main.620.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38939379
Code
 ShamikRoy/Subframe-Prediction