Interdisciplinary Lessons Learned While Researching Fake News - Sample, Char; Jensen, Michael J.; Scott, Keith; McAlaney, John; Fitchpatrick, Steve; Brockinton, Amanda; Ormrod, David; Ormrod, Amy (Frontiers in Psychology, 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 a new phenomenon (McKernon 1925) and it has been applied to a variety of distinct phenomenon ranging from satire to news which one finds disagreeable (Jankowski 2018; Tandoc, Lim, and Ling 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). Fake news was internationally recognized as a problem in 2017, although this latest version had been identified earlier (Pomerantsev and Weiss 2014; Applebaum and Lucas 2016). There are many initiatives to counter this problem and the results have had varying levels of success (Flanagin and Metzger, 2014; Horne and Adali 2017; Sample et al. 2018). The inability to create a complete solution continues to stymie researchers and other vested parties alike. 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 manner in which the data is designed for consumption relies on a mixture of psychology, sociology, political science, economics, linguistics, marketing and in some cases fine arts. The authors for this effort discuss deception’s history, both old and new, from an interdisciplinary viewpoint, 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 photos 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 that can be incorporated to improve outcomes. A3-point evaluation model that focuses on contextual evaluation, pattern-spread and archival analysis of both the author and publication archives will be 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.