Abstract
The Internet is the premier platform for the proliferation of news. Online news is unstructured narrative text that embeds facts, frames, and bias that can influence society about critical issues. Online news sources are carriers of news that operate within complex and dynamic networks. The informational flows, interactions, and structural variations of online news lead to asymmetries and influence societal attitudes and beliefs. Current efforts to express the internal embeddedness in online news text are limited by the use of existing computational tools. This research has the potential to inform advanced machine learning and to help researchers to understand implicit structural embeddedness to address real-world critical issues. This paper establishes the new concept of double subjectivity, proposes a formal definition of a news frame issues network, and introduces a social influence model for the discovery of distinct pathways. The paper also exposes opinion shifts in complex networks for future computational treatment of narrative text.
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