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

TL;DR

SUFA introduces a semantic-relations-based unsupervised framing analysis using dependency parsing to identify and analyze entity-centric frames in news coverage. The approach combines qualitative and computational methods across a gun-violence dataset, demonstrating that semantic relations capture framing nuances beyond bag-of-words models. A seven-step SUFA procedure accommodates optional qualitative input and scalable computational analysis, enabling identification of frames without pre-defined schemas. Overall, SUFA offers a flexible, unsupervised framework with broad applicability for measuring framing effects across social science and computational domains, while acknowledging manual input requirements and scope limitations regarding non-textual framing cues.

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.