Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames

• URL: http://arxiv.org/abs/2104.11030v1
• Date: Thu, 22 Apr 2021 13:05:53 GMT
• Title: Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames
• Authors: Shima Khanehzar, Trevor Cohn, Gosia Mikolajczak, Andrew Turpin, Lea Frermann
• Abstract summary: We develop a novel semi-supervised model for understanding how news media frame political issues. The model learns to embed local information about the events and related actors in a news article through an auto-encoding framework. Our experiments show that our model outperforms previous models of frame prediction.
• Score: 32.06056273913706
• License: http://creativecommons.org/licenses/by/4.0/
• 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: our model outperforms previous models of frame prediction; we can further improve performance with unlabeled training data leveraging the semi-supervised nature of our model; and the learnt event and actor embeddings intuitively corroborate the document-level predictions, providing a nuanced and interpretable article frame representation.

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