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

## introduction journalists often aim to package complex realworld events into comprehensive narratives, following a logical sequence of events involving a limited set of actors. constrained by word limits, they necessarily select some facts over others, and make certain perspectives more salient. this phenomenon of framing, be it purposeful or unconscious, has been thoroughly studied in the social and political sciences @xcite . more recently, the natural language processing community has taken an interest in automatically predicting the frames of news articles @xcite @xcite @xcite . definitions of framing vary widely including: expressing the same semantics in different forms (equivalence framing); presenting selective facts and aspects (emphasis framing); and using established syntactic and narrative structures to convey information (story framing) @xcite . the model presented in this work builds on the concepts of emphasis framing and story framing, predicting the global (aka. primary) frame of a news article on the basis of the events and participants it features. primary frame prediction has attracted substantial interest recently with the most accurate models being supervised classifiers built on top of large pretrained language models @xcite . this work advances prior work in two ways. first, we explicitly incorporate a formalization of story framing into our frame prediction models. by explicitly modeling news stories as latent representations over events and related actors, we obtain interpretable, latent representations lending transparency to our frame prediction models. we argue that transparent machine learning is imperative in a potentially sensitive domain like automatic news analysis, and show that the local, latent labels inferred by our model lend explanatory power to its frame predictions. secondly, the latent representations are induced without frame-level supervision, requiring only a pre-trained, off-the-shelf semantic role labeling (srl) model @xcite . this renders our frame prediction models semi-supervised, allowing us to use large unlabeled news corpora. more technically, we adopt a dictionary learning framework with deep autoencoders through which we learn to map events and their agents and patients 1 independently into their respective structured latent space. our model thus learns a latent multi-view representation of news stories, with each view contributing evidence to the primary frame prediction from its own perspective. we incorporate the latent multi-view representation into a transformer-based document-level frame classification model to form a semi-supervised model, in which the latent representations are jointly learnt with the classifier. we demonstrate empirically that our semisupervised model outperforms current state-of-theart models in frame prediction. more importantly, through detailed qualitative analysis, we show how our latent features mapped to events and related actors allow for a nuanced analysis and add interpretability to the model predictions foot_1 . in summary, our contributions are: ## background and related work a widely accepted definition of frames describes them as a selection of aspects of perceived reality, which are made salient in a communicating context to promote a particular problem definition, causal interpretation, moral evaluation and the treatment recommendation for the described issue @xcite . while detecting media frames has attracted much attention and spawned a variety of methods, it poses several challenges for automatic prediction due to its vagueness and complexity. two common approaches in the study of frames focus either on the detailed issue-specific elements of a frame or, somewhat less nuanced, on generic framing themes prevalent across issues. within the first approach, @xcite developed a manual coding scheme, relying on entman's definition @xcite . while the scheme assumes that each frame is composed of common elements, categories within those elements are often specific to the particular issue being discussed (e.g., "same sex marriage" or "gun control"), making comparison across different issues, and detecting them automatically difficult. similarly, earlier studies focusing specifically on unsupervised models to extract frames, usually employed topic modeling @xcite @xcite to find the issue-specific frames, limiting across-issue comparisons. studies employing generic frames address this shortcoming by proposing common categories applicable to different issues. for example, boydstun et al. (2013) proposed a list of 15 broad frame categories commonly used when discussing different policy issues, and in different communication contexts. the media frames corpus (mfc; card et al. ( 2015 )) includes about 12,000 news articles from 13 u.s. newspapers covering five different policy issues, annotated with the dominant frame from @xcite . recent methods have been expanded to multilingual frame detection. @xcite used the mfc to investigate framing in russian news. they introduced embedding-based methods for projecting frames of one language into another (i.e., english to russian). akyürek et al. (2020) studied multilingual transfer learning to detect multiple frames in target languages with few or no annotations. recently, huguet cabot et al. (2020) investigated joint models incorporating metaphor, the proposal, backed by high-tech companies, would raise the limit of so-called h-1b visas granted each year to skilled workers from abroad. emotion and political rhetoric within multi-task learning to predict framing of policy issues. our modelling approach is inspired by recent advances in learning interpretable latent representations of the participants and relationships in fiction stories. @xcite present relationship modelling networks (rmns), which induce latent descriptors of types of relationships between characters in fiction stories, in an unsupervised way. rmns combine dictionary learning with deep autoencoders, and are trained to effectively encode text passages as linear combinations over latent descriptors, each of which corresponds to a distinct relationship (not unlike topics in a topic model). @xcite extend the idea to a multi-view setup, jointly learning multiple dictionaries, which capture properties of individual characters in addition to relationships. we adopt this methodology for modeling news articles through three latent views: capturing their events (predicates), and participants (arg0, arg1). we combine the unsupervised autoencoder with a frame classifier into an interpretable, semi-supervised framework for article-level frame prediction. ## semi-supervised interpretable frame classification in this section, we present our frame classifier, which is interpretable and semi-supervised (friss). the full model is visualized in figure 1 . given a corpus of news articles, some of which have a label indicating their primary frame y (figure 1(a)), friss learns to predict ŷ for each document by combining a supervised classification module (figure 1(c) ) and an unsupervised foot_2 autoencoding module (figure 1(b) ), which are jointly trained. the unsupervised module (i) can be trained with additional unlabeled training data, which improves performance (section 5.2); and ## evaluation in this section, we evaluate the performance of friss on primary frame prediction for issuespecific news articles against prior work (sec 5.1), demonstrate the benefit of adding additional unlabeled data to our semi-supervised model (sec 5.2), and present a qualitative analysis of our model output corroborating its interpretability (sec 5.3). ## conclusion we presented friss, an interpretable model of media frame prediction, incorporating notions of emphasis framing (selective highlighting of issue aspects) and story framing (drawing on the events and actors described in an article). our semisupervised model predicts article-level frame of news articles, leveraging local predicate and argument level embeddings. we demonstrated its three-fold advantage: first, our model empirically outperforms existing models for frame classification; second, it can effectively leverage additional unlabeled data further improving performance; and, finally, its latent representations add transparency to classifier predictions and provide a nuanced article representation. the analyses provided by our model can support downstream applications such as automatic, yet transparent, highlighting of re-porting patterns across countries or news outlets; or frame-guided summarization which can support both frame-balanced or frame-specific news summaries. in future work, we plan to extend our work to more diverse news outlets and policy issues, and explore richer latent models of article content, including graph representations over all involved events and actors.