  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.
