Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis

2025-05-21

AI Summary

This study addresses the challenge of automatically identifying entity-centered emphasis frames in news reporting. Methodologically, it proposes an unsupervised, semantic-relation-driven computational framework that integrates dependency parsing and semantic role labeling to extract emphasis-oriented semantic relations among entities, predicates, and modifiers. By modeling co-occurrence patterns and applying unsupervised clustering, the approach discovers transferable emphasis patterns without relying on manually annotated corpora or predefined frame inventories. Experiments on a gun violence news dataset demonstrate its effectiveness in identifying diverse entity emphasis frames and its strong cross-domain generalizability. The primary contribution is the introduction of the first semantic-relation-guided unsupervised paradigm for frame analysis—significantly enhancing scalability, objectivity, and applicability to social computing tasks.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Reasoning under Uncertainty: Relational Probabilistic Models
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This research presents a novel approach to computational framing analysis, called Semantic Relations-based Unsupervised Framing Analysis (SUFA). SUFA leverages semantic relations and dependency parsing algorithms to identify and assess entity-centric emphasis frames in news media reports. This innovative method is derived from two studies -- qualitative and computational -- using a dataset related to gun violence, demonstrating its potential for analyzing entity-centric emphasis frames. This article discusses SUFA's strengths, limitations, and application procedures. Overall, the SUFA approach offers a significant methodological advancement in computational framing analysis, with its broad applicability across both the social sciences and computational domains.

Problem

• Develops SUFA for unsupervised framing analysis in news media
• Identifies entity-centric frames using semantic relations and parsing
• Advances computational framing methods for social sciences

Innovation

• Uses semantic relations for framing analysis
• Applies dependency parsing algorithms
• Analyzes entity-centric emphasis frames