"Fake News" Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content. (February 2021)

• Record Type: Journal Article
• Authors: Molina, Maria D.; Sundar, S. Shyam; Le, Thai; Lee, Dongwon
• Other Names: Kim Jeong-Nam guest-editor.; Gil de Zúñiga Homero guest-editor.
• Is Part Of: American behavioral scientist. Volume 65:Number 2(2021)
• Page Start: 180
• Page End: 212
• Publication Date: 2021-02
• Subjects: fake news -- false information -- misinformation -- persuasive information; Social sciences -- Periodicals; Political science -- Periodicals; United States -- Social conditions -- Periodicals; United States -- Politics and government -- Periodicals; 300
• DOI: 10.1177/0002764219878224
• Languages: English
• ISSNs: 0002-7642

Abstract

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.