Conference Proceedings
Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames
S Khanehzar, T Cohn, G Mikolajczak, A Turpin, L Frermann
Naacl Hlt 2021 2021 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Proceedings of the Conference | ASSOC COMPUTATIONAL LINGUISTICS-ACL | Published : 2021
Abstract
Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on classifying the frame of a full news article while framing signals are often subtle and local. Furthermore, automatic news analysis is a sensitive domain, and existing classifiers lack transparency in their predictions. This paper addresses both issues with a novel semi-supervised model, which jointly learns to embed local information about the events and related actors in a news article through an auto-encoding framework, and to leverage this signal for document-level frame classification. Our experiments show that: ou..
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Awarded by Melbourne School of Engineering, University of Melbourne
Funding Acknowledgements
We thank the anonymous reviewers for their helpful feedback and suggestions. This article was written with the support from the graduate research scholarship from the Melbourne School of Engineering, University of Melbourne provided to the first author. The original news articles used in this work were obtained from Lexis Nexis under the institutional licence held by the University of Melbourne. This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne, established with the assistance of LIEF Grant LE170100200.