Mining temporal discriminant frames via joint matrix factorization: A case study of illegal immigration in the U.S. news media

Framing detection has emerged to be an important topic in recent natural language processing research. Although several frameworks have been proposed, little is known about how to detect temporal discriminant frames. This study proposes a framework for discovering temporal discriminant frames, with a focus on identifying emergent frames in news discussions of illegal immigration issue. Built on joint non-negative matrix factorization (NMF), we propose the njNMF algorithm, an improved joint matrix factorization algorithm, to detect the temporal frames. We conducted experiments using the njNMF algorithm to identify emergent frames. The results of our experiments show that framing of illegal immigration changes over time, from human trafficking frames, to more recent economic and criminality frames. These findings suggest the utility of our temporal framing approach and can be used as a framing detection tool for policy researchers to understand the role of news framing in public agenda setting.