Exploring an Alternative Computational Approach for News Framing Analysis Through Community Detection in Framing Element Networks

This study introduces the “framing element” method, an alternative approach to computational news framing detection. Rooted in a constructionist framing analysis framework, it identifies frames as packages of framing elements, including actors (individuals and organizations) and topics, extending beyond topic-focused methods in prior unsupervised analyses. Compared with latent Dirichlet allocation (LDA)- and Bidirectional Encoder Representations from Transformers (BERT)-based approaches on 1,300 U.S. gun violence news articles, this method addresses LDA’s limitations by focusing on high-level framing elements rather than keywords and is less labor-intensive than BERT-based supervised learning. Supporting both inductive and deductive analyses, it achieves comparable results to LDA while uncovering a previously unidentified gun violence frame.