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

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 semisupervised 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. 1 We experiment with three types of semantic roles: predicates and associated arguments (ARG0 and ARG1), however, our framework is agnostic to the types of semantic roles, and can further incorporate other types of semantic roles or labels.

Controlled Neural Sentence-Level Reframing of News Articles

“…A more specific definition, which targets the argumentation contexts, defines a frame as a set of arguments that share an aspect (Ajjour et al, 2019). As for frame classification, most of the proposed approaches (Naderi and Hirst, 2017;Hartmann et al, 2019;Khanehzar et al, 2021) employ the media frames corpus (Card et al, 2015), which is built upon the framing scheme of Boydstun et al (2013). Following these approaches, we utilize the media frames corpus to build the dataset for our task.…”