"Fake News" Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content
"Fake News" Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content
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DOI：
10.1177/0002764219878224
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发表时间：
2019-10-14
影响因子：
3.2
通讯作者：
Lee, Dongwon
中科院分区：
文献类型：
--
作者：
Molina, Maria D.;Sundar, S. Shyam;Lee, Dongwon
随着“假新闻”的祸害继续困扰着我们的信息环境，人们的注意力转向了设计自动解决方案来检测有问题的在线内容。但是，为了建立可靠的算法来标记“假新闻”，我们需要超越对这个概念的广泛定义，并识别足够具体的区别特征，以供机器学习。考虑到这一目标，我们对“假新闻”进行了解释，作为一个概念，它已经膨胀到不仅仅包括虚假信息，游击队将其武器化，以诽谤那些政治上反对它们的人所说的话的真实性。我们在“假新闻”的标签下识别了七种不同类型的在线内容(虚假新闻、两极分化内容、讽刺、错误报道、评论、有说服力的信息和公民新闻)，并通过在四个领域--消息、来源、结构和网络--引入操作指标的分类，将它们与“真实新闻”进行对比，这四个领域共同有助于消除在线新闻内容的性质的歧义。
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.