000			01694nam a22001577a 4500
999			_c518560 _d518560
008			210929b ||||| |||| 00| 0 eng d
100			_aMolina, M.D., Sundar, S.S. Le, T. and Lee, D. _929628
245			_a“Fake news” is not simply false information: A concept explication and taxonomy of online content
260			_aAmerican Behavioral Scientist
300			_a65(2), Feb, 2021: p.180-212
520			_aAs 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.
650			_aFake news, False information, Misinformation, Persuasive information _927806
773			_aAmerican Behavioral Scientist
906			_aMASS MEDIA
942			_cAR