Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis
It provides a methodological advancement for researchers in social sciences and computational domains, though it appears incremental as it builds on existing parsing techniques.
The paper tackles computational framing analysis by introducing SUFA, a method using semantic relations and dependency parsing to identify entity-centric emphasis frames in news media, demonstrated on a gun violence dataset.
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.