PhD Thesis Proposal: Jeremy E. Thompson

Thursday, December 20, 2018, 1:00–3:00pm

Rm. 201, MacLean ESC (Rett's Room)

“Critical Components of Fake News”

Abstract

Fake news has become a serious concern, as distributing misinformation has become a trivial endeavor with noticeable effect. Social media’s attempts to remedy the spread of fake news have yielded only modest results. A solution is critically required, but choosing the right solution is perhaps just as critical. One solution would be to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as in order for it to be banned it must first be identified. What is proposed is a method to automatically recognize suspected fake news, and to provide news consumers, as well as researchers, historians, and journalists, with more information as to its veracity. The emphasis here is on the claim of “news”. For example, opinion pieces are not news, but opinion pieces in the guise of news are viewed as “fake news.”

It is suggested that fake news is comprised of two primary components: premises and misleading content. Those two pieces form a foundational framework to evaluate news items. A fake news piece can be condensed down to a collection of premises, which may or may not be true, and to various forms of misleading material, including biased arguments and language, misdirection, and manipulation. Through this lens, deceptions, via either false premises or misleading arguments, are more apparent. The premises, standing alone and highlighted, can be evaluated for their basis in truth and according to the beliefs of the consumer. The misleading content can be exposed for whatever biases it contains, regardless of the intent of the author. The value of this framework lies not only in its practical utility to the public, but also in the light it will shed on the prevalence of bias and the absence of balance in many news stories. Expected contributions include a working definition for fake news, a framework for recognizing fake news according to its components, the framework’s ability to assess historical news sources to measure trends in journalistic objectivity over time, its applicability to plainly identify the undermining of science with biased reporting and pseudoscience, and an improved understanding of misleading information in media.

Thesis Committee

For more information, contact Daryl Laware at daryl.a.laware@dartmouth.edu.