A social influence model for exploring double subjectivity through news frames in online news

Loretta H. Cheeks, Ashraf Gaffar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publication2017 Intelligent Systems Conference, IntelliSys 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9781509064359
StatePublished - Mar 23 2018
Event2017 Intelligent Systems Conference, IntelliSys 2017 - London, United Kingdom
Duration: Sep 7 2017Sep 8 2017


Other2017 Intelligent Systems Conference, IntelliSys 2017
Country/TerritoryUnited Kingdom


  • Artificial intelligence
  • bias
  • data mining
  • framing
  • machine learning
  • natural language processing
  • news source sentiment
  • sentiment analysis
  • social influence
  • subjectivity

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Optimization


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