无需标注数据，通过语义关系自动识别新闻中对特定主体的强调框架。
Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis
- 基于语义关系与依存句法分析，挖掘文本中主体聚焦的强调模式。
- 在枪支暴力新闻数据集上验证，能有效识别实体中心性强调框架。
- 适合社会科学研究者和计算传播学从业者用于自动化内容分析。
本研究提出一种新型计算框架分析方法——基于语义关系的无监督框架分析（SUFA）。该方法利用语义关系与依存句法分析算法，识别新闻报道中以实体为中心的强调框架。研究基于两项定性和计算性研究，使用枪支暴力相关数据集进行验证，展示了其在分析实体中心性强调框架方面的潜力。文章还讨论了SUFA的优势、局限性及应用流程。总体而言，该方法在计算框架分析领域具有显著的方法论进步，适用于社会科学与计算领域的广泛场景。
原文摘要 · Abstract (English)
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.
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