Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Media Framing through the Lens of Event-Centric Narratives

Rohan Das, Aditya Chandra, I-Ta Lee, Maria Leonor Pacheco

Abstract

From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on the economy, and thus communicate very different interpretations of the same phenomenon. In this work, we argue that to explain framing devices we have to look at the way narratives are constructed. As a first step in this direction, we propose a framework that extracts events and their relations to other events, and groups them into high-level narratives that help explain frames in news articles. We show that our framework can be used to analyze framing in U.S. news for two different domains: immigration and gun control.

Media Framing through the Lens of Event-Centric Narratives

Abstract

From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on the economy, and thus communicate very different interpretations of the same phenomenon. In this work, we argue that to explain framing devices we have to look at the way narratives are constructed. As a first step in this direction, we propose a framework that extracts events and their relations to other events, and groups them into high-level narratives that help explain frames in news articles. We show that our framework can be used to analyze framing in U.S. news for two different domains: immigration and gun control.
Paper Structure (38 sections, 4 equations, 3 figures, 10 tables)

This paper contains 38 sections, 4 equations, 3 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Computational Framing Analysis
  4. Narrative Representations
  5. Extracting Narratives
  6. Extracting Events
  7. Extracting Relations
  8. Clustering Narrative Chains
  9. Analysis
  10. Datasets
  11. Quality of Narratives
  12. Relation Prediction
  13. Narrative Clustering
  14. Explaining Frames with Narratives
  15. Frame Prediction
  16. ...and 23 more sections

Figures (3)

  • Figure 1: Motivating example for grouping narratives. Verbs are in bold. Objects are underlined. Relations are highlighted. Colors indicate narrative clusters. Capitalizations indicate Boydstun2014 policy frames.
  • Figure 2: Relation Classifier Architecture
  • Figure 3: Frame prediction results on the immigration and gun control datasets using only cluster features. The model powered by Narrative clusters (with LLM expansions) outperforms four baselines: (1) Random, (2) LDA topics, (3) Event Types, and (4) Narrative Clusters (w/o LLM expansions).