01694nam a22001577a 4500 518560 518560 210929b ||||| |||| 00| 0 eng d Molina, M.D., Sundar, S.S. Le, T. and Lee, D. 29628 “Fake news” is not simply false information: A concept explication and taxonomy of online content American Behavioral Scientist 65(2), Feb, 2021: p.180-212 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 27806 American Behavioral Scientist MASS MEDIA AR 0 0 ddc 0 0 392629 IIPA IIPA 2021-09-29 65(2), Feb, 2021: p.180-212 AR125690 2021-09-29 AR