FRaN-X: FRaming and Narratives-eXplorer
🤖 AI Summary
This work addresses the challenge of automatically identifying and framing entity narrative roles (e.g., protagonist, antagonist, innocent victim) in media texts. Methodologically, it introduces the first fine-grained, cross-lingual, and interpretable narrative analysis system: (1) a novel 22-category role taxonomy; (2) a two-stage sequence labeling and role classification model built upon multilingual pretrained language models; and (3) integrated interactive knowledge graphs, cross-document frame comparison, and timeline-based evolutionary visualization. The system supports Bulgarian, English, Hindi, Russian, and Portuguese, and demonstrates empirical efficacy in analyzing narratives around the Russia–Ukraine conflict and climate change. It is open-sourced and deployed as a real-time online analytical platform. Key contributions include: the first fine-grained narrative role taxonomy; multilingual, dynamic entity role tracking; and an interpretable, frame-comparative analytical paradigm tailored for media discourse analysis.
Application Category
📝 Abstract
We present FRaN-X, a Framing and Narratives Explorer that automatically detects entity mentions and classifies their narrative roles directly from raw text. FRaN-X comprises a two-stage system that combines sequence labeling with fine-grained role classification to reveal how entities are portrayed as protagonists, antagonists, or innocents, using a unique taxonomy of 22 fine-grained roles nested under these three main categories. The system supports five languages (Bulgarian, English, Hindi, Russian, and Portuguese) and two domains (the Russia-Ukraine Conflict and Climate Change). It provides an interactive web interface for media analysts to explore and compare framing across different sources, tackling the challenge of automatically detecting and labeling how entities are framed. Our system allows end users to focus on a single article as well as analyze up to four articles simultaneously. We provide aggregate level analysis including an intuitive graph visualization that highlights the narrative a group of articles are pushing. Our system includes a search feature for users to look up entities of interest, along with a timeline view that allows analysts to track an entity's role transitions across different contexts within the article. The FRaN-X system and the trained models are licensed under an MIT License. FRaN-X is publicly accessible at https://fran-x.streamlit.app/ and a video demonstration is available at https://youtu.be/VZVi-1B6yYk.
Problem
Research questions and friction points this paper is trying to address.
Automatically detect entity mentions and classify narrative roles from text
Analyze entity framing as protagonists, antagonists, or innocents in media
Support multilingual and multi-domain framing analysis for media comparison
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-stage system for entity role classification
Supports five languages and two domains
Interactive web interface with visualization tools
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