用Transformer模型自动识别英文新闻中的议题框架。
Finding frames with BERT: A transformer-based approach to generic news frame detection
- 基于BERT的模型分析英文网络内容中的议题框架
- 在多个新闻数据集上达到超过75%的准确率
- 适合媒体研究与人工智能交叉领域的学者
议题框架是传播科学中广泛使用的概念。数字数据的可获得性为研究在线传播中社会现实特定方面如何被凸显提供了新可能，但也带来了框架分析规模化及向新研究领域（如人工智能系统对重要社会议题表述的影响）迁移的挑战。为此，本文提出一种基于Transformer的通用新闻议题框架检测方法，适用于英语网络内容。文中讨论了训练与测试数据集的构建、模型架构设计及方法验证，并反思了自动化检测通用新闻议题框架的潜力与局限。
原文摘要 · Abstract (English)
Framing is among the most extensively used concepts in the field of communication science. The availability of digital data offers new possibilities for studying how specific aspects of social reality are made more salient in online communication but also raises challenges related to the scaling of framing analysis and its adoption to new research areas (e.g. studying the impact of artificial intelligence-powered systems on representation of societally relevant issues). To address these challenges, we introduce a transformer-based approach for generic news frame detection in Anglophone online content. While doing so, we discuss the composition of the training and test datasets, the model architecture, and the validation of the approach and reflect on the possibilities and limitations of the automated detection of generic news frames.
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