Abstract
The Internet is a major source of online news content. Current efforts are made to unlock latent meaning in online news content using advanced language processing tools and machine intelligence. This necessitates exploring the internal structure of news narratives to cope with the challenges posed by limitations of existing tools. This article explores the conceptualization of Double Subjectivity in news frames as deployed by online news sources. We propose a new perspective by exploring a) a News Frame Issues Network that is useful for describing the structure of online news media and b) formulating an influence model for understanding the dynamics of bias that underpins Double Subjectivity. This research has the potential to inform more intelligent conclusions about narrative text meaning (or semantics) to address real-world socio-environmental issues. We use water insecurity in the Southwestern United States as our contextual case. Our experimental evaluation shows the proposed network and model is an effective approach for advancing what we know about the production of language in narrative text where subjectivity exist.
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