The Media Frames Corpus: Annotations of Frames Across Issues

By Dallas Card, Amber E. Boydstun, Justin H. Gross, Philip Resnik, Noah A. Smith. 2015

286 citations recorded by the source corpus.

We describe the first version of the Media Frames Corpus: several thousand news articles on three policy issues, annotated in terms of media framing. We motivate framing as a phenomenon of study for computational linguistics and describe our annotation process.

Subfields: Artificial Intelligence, General Social Sciences, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

ChatGPT outperforms crowd workers for text-annotation tasks (2023, background): “The problem frame, on the other hand, describes all tweets framing content moderation as a problem on its own as well as to other issues (e.g., free speech); (5) policy frame detection (“frames II”): whether a tweet contains a set of fourteen policy frames proposed in (Card et al., 2015).”
A Framework for the Computational Linguistic Analysis of Dehumanization (2020, background): “For example, some work has drawn upon political communication theory to automatically detect the framing of an issue (Entman, 1993; Boydstun et al., 2013; Card et al., 2015) through both supervised classification (Boydstun et al.”
Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2021, background): “Card et al. (2015) provide a dataset of news articles annotated with framing dimensions.”
Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media (2021, background): “Intuitively, many news events are not just relevant to one political ideology, although they may differ in the way they are framed (Card et al., 2015; Demszky et al., 2019; Hofmann et al., 2021b).”
In Plain Sight: Media Bias Through the Lens of Factual Reporting (2019, background): “The relationship between framing and news is investigated by Card et al. (2015), in which news articles are annotated with framing dimensions like “legal-ity” and “public opinion.””
Introducing CAD: the Contextual Abuse Dataset (2021, methodology): “Breitfeller et al. (2019) use a collaborative multi-stage process to label micro-aggression and Card et al. (2015) use a similar process for labelling news articles.”
Evaluating the Stability of Embedding-based Word Similarities (2018, background): “Such bias may result in measurable differences in word association due to framing effects (Card et al., 2015), which could be observable by comparing the words associated with a given query term.”