Sample Char, Jensen Michael J, Scott Keith, McAlaney John, Fitchpatrick Steve, Brockinton Amanda, Ormrod David, Ormrod Amy
Idaho National Laboratory, Idaho Falls, ID, United States.
Institute for Governance and Policy Analysis, University of Canberra, Canberra, ACT, Australia.
Front Psychol. 2020 Dec 16;11:537612. doi: 10.3389/fpsyg.2020.537612. eCollection 2020.
The misleading and propagandistic tendencies in American news reporting have been a part of public discussion from its earliest days as a republic (Innis, 2007; Sheppard, 2007). "Fake news" is hardly new (McKernon, 1925), and the term has been applied to a variety of distinct phenomenon ranging from satire to news, which one may find disagreeable (Jankowski, 2018; Tandoc et al., 2018). However, this problem has become increasingly acute in recent years with the Macquarie Dictionary declaring "fake news" the word of the year in 2016 (Lavoipierre, 2017). The international recognition of fake news as a problem (Pomerantsev and Weiss, 2014; Applebaum and Lucas, 2016) has led to a number of initiatives to mitigate perceived causes, with varying levels of success (Flanagin and Metzger, 2014; Horne and Adali, 2017; Sample et al., 2018). The inability to create a holistic solution continues to stymie researchers and vested parties. A significant contributor to the problem is the interdisciplinary nature of digital deception. While technology enables the rapid and wide dissemination of digitally deceptive data, the design and consumption of data rely on a mixture of psychology, sociology, political science, economics, linguistics, marketing, and fine arts. The authors for this effort discuss deception's history, both old and new, from an interdisciplinary viewpoint and then proceed to discuss how various disciplines contribute to aiding in the detection and countering of fake news narratives. A discussion of various fake news types (printed, staged events, altered photographs, and deep fakes) ensues with the various technologies being used to identify these; the shortcomings of those technologies and finally the insights offered by the other disciplines can be incorporated to improve outcomes. A three-point evaluation model that focuses on contextual data evaluation, pattern spread, and archival analysis of both the author and publication archives is introduced. While the model put forth cannot determine fact from fiction, the ability to measure distance from fact across various domains provides a starting point for evaluating the veracity of a new story.
自美国建国初期成为共和国以来，美国新闻报道中存在的误导性和宣传性倾向就一直是公众讨论的一部分（英尼斯，2007年；谢泼德，2007年）。“假新闻”并非新鲜事物（麦克农，1925年），这个词已被应用于从讽刺作品到新闻等各种不同的现象，人们可能会觉得这些现象令人不快（扬科夫斯基，2018年；坦多克等人，2018年）。然而，近年来这个问题变得愈发尖锐，《麦夸里词典》将“假新闻”评为2016年度词汇（拉沃皮埃尔，2017年）。国际社会将假新闻视为一个问题（波梅兰采夫和韦斯，2014年；阿普尔鲍姆和卢卡斯，2016年），这引发了一系列旨在减轻其可感知成因的举措，但成效各异（弗拉纳根和梅茨格，2014年；霍恩和阿达利，2017年；桑普尔等人，2018年）。无法找到一个全面的解决方案继续阻碍着研究人员和既得利益方。这个问题的一个重要因素是数字欺骗的跨学科性质。虽然技术使得数字欺骗性数据能够迅速广泛传播，但数据的设计和消费依赖于心理学、社会学、政治学、经济学、语言学、市场营销和美术等多学科的综合作用。参与这项工作的作者们从跨学科的视角探讨了欺骗的新旧历史，接着讨论了各学科如何助力检测和对抗假新闻叙事。随后讨论了各种假新闻类型（印刷品、 staged 事件、篡改照片和深度伪造）以及用于识别这些类型的各种技术；这些技术的缺点，最后是其他学科提供的见解如何能被整合以改善结果。引入了一个三点评估模型，该模型侧重于对上下文数据评估、模式传播以及作者和出版物档案的档案分析。虽然提出的这个模型无法辨别事实与虚构，但能够衡量与各领域事实的距离，这为评估新报道的真实性提供了一个起点。 （注：“staged events”不太明确准确意思，这里暂直译为“staged事件”）
