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

Loretta H. Cheeks; Ashraf Gaffar

doi:10.1109/IntelliSys.2017.8324285

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 proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	2017 Intelligent Systems Conference, IntelliSys 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	23-33
Number of pages	11
Volume	2018-January
ISBN (Electronic)	9781509064359
DOIs	https://doi.org/10.1109/IntelliSys.2017.8324285
State	Published - Mar 23 2018
Event	2017 Intelligent Systems Conference, IntelliSys 2017 - London, United Kingdom Duration: Sep 7 2017 → Sep 8 2017

Other

Other	2017 Intelligent Systems Conference, IntelliSys 2017
Country/Territory	United Kingdom
City	London
Period	9/7/17 → 9/8/17

Keywords

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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10.1109/IntelliSys.2017.8324285

Cite this

Cheeks, LH & Gaffar, A 2018, A social influence model for exploring double subjectivity through news frames in online news. in 2017 Intelligent Systems Conference, IntelliSys 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 23-33, 2017 Intelligent Systems Conference, IntelliSys 2017, London, United Kingdom, 9/7/17. https://doi.org/10.1109/IntelliSys.2017.8324285

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title = "A social influence model for exploring double subjectivity through news frames in online news",

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.",

keywords = "Artificial intelligence, bias, data mining, framing, machine learning, natural language processing, news source sentiment, sentiment analysis, social influence, subjectivity",

author = "Cheeks, {Loretta H.} and Ashraf Gaffar",

year = "2018",

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doi = "10.1109/IntelliSys.2017.8324285",

language = "English (US)",

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AU - Cheeks, Loretta H.

AU - Gaffar, Ashraf

PY - 2018/3/23

Y1 - 2018/3/23

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - bias

KW - data mining

KW - framing

KW - machine learning

KW - natural language processing

KW - news source sentiment

KW - sentiment analysis

KW - social influence

KW - subjectivity

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BT - 2017 Intelligent Systems Conference, IntelliSys 2017

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

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

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Keywords

ASJC Scopus subject areas

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