TY - JOUR
T1 - “Fake News” Is Not Simply False Information
T2 - A Concept Explication and Taxonomy of Online Content
AU - Molina, Maria D.
AU - Sundar, S. Shyam
AU - Le, Thai
AU - Lee, Dongwon
N1 - Funding Information:
Molina Maria D. 1 Sundar S. Shyam 1 Le Thai 1 Lee Dongwon 1 1 Penn State University, University Park, PA, USA Maria D. Molina, Media Effects Research Laboratory, Donald P. Bellisario College of Communications, Penn State University, 115 Carnegie Building, University Park, PA 16802, USA. Email: mdm63@psu.edu 10 2019 0002764219878224 © 2019 SAGE Publications 2019 SAGE Publications As the scourge of “fake news” continues to plague our information environment, attention has turned toward devising automated solutions for detecting problematic online content. But, in order to build reliable algorithms for flagging “fake news,” we will need to go beyond broad definitions of the concept and identify distinguishing features that are specific enough for machine learning. With this objective in mind, we conducted an explication of “fake news” that, as a concept, has ballooned to include more than simply false information, with partisans weaponizing it to cast aspersions on the veracity of claims made by those who are politically opposed to them. We identify seven different types of online content under the label of “fake news” (false news, polarized content, satire, misreporting, commentary, persuasive information, and citizen journalism) and contrast them with “real news” by introducing a taxonomy of operational indicators in four domains—message, source, structure, and network—that together can help disambiguate the nature of online news content. fake news false information misinformation persuasive information national science foundation https://doi.org/10.13039/100000001 Standard Grant No. CNS-1742702. edited-state corrected-proof Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the U. S. National Science Foundation (NSF) via Standard Grant No. CNS-1742702.
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the U. S. National Science Foundation (NSF) via Standard Grant No. CNS-1742702.
Publisher Copyright:
© 2019 SAGE Publications.
PY - 2021/2
Y1 - 2021/2
N2 - As the scourge of “fake news” continues to plague our information environment, attention has turned toward devising automated solutions for detecting problematic online content. But, in order to build reliable algorithms for flagging “fake news,” we will need to go beyond broad definitions of the concept and identify distinguishing features that are specific enough for machine learning. With this objective in mind, we conducted an explication of “fake news” that, as a concept, has ballooned to include more than simply false information, with partisans weaponizing it to cast aspersions on the veracity of claims made by those who are politically opposed to them. We identify seven different types of online content under the label of “fake news” (false news, polarized content, satire, misreporting, commentary, persuasive information, and citizen journalism) and contrast them with “real news” by introducing a taxonomy of operational indicators in four domains—message, source, structure, and network—that together can help disambiguate the nature of online news content.
AB - As the scourge of “fake news” continues to plague our information environment, attention has turned toward devising automated solutions for detecting problematic online content. But, in order to build reliable algorithms for flagging “fake news,” we will need to go beyond broad definitions of the concept and identify distinguishing features that are specific enough for machine learning. With this objective in mind, we conducted an explication of “fake news” that, as a concept, has ballooned to include more than simply false information, with partisans weaponizing it to cast aspersions on the veracity of claims made by those who are politically opposed to them. We identify seven different types of online content under the label of “fake news” (false news, polarized content, satire, misreporting, commentary, persuasive information, and citizen journalism) and contrast them with “real news” by introducing a taxonomy of operational indicators in four domains—message, source, structure, and network—that together can help disambiguate the nature of online news content.
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U2 - 10.1177/0002764219878224
DO - 10.1177/0002764219878224
M3 - Article
AN - SCOPUS:85074367692
SN - 0002-7642
VL - 65
SP - 180
EP - 212
JO - American Behavioral Scientist
JF - American Behavioral Scientist
IS - 2
ER -