Fake News: Concepts, Rhetorical Disruptions, and Policy Implications

Mon, 27 Oct 2025 00:26:53 +0000

Media, Society, and Culture Lesson Module

Seeing Isn’t Believing

 RepresentUs, CC BY 3.0 <https://creativecommons.org/licenses/by/3.0>, via Wikimedia Commons: A synthetic Kim Jong Un warns Americans—evidence that our eyes/ears are no longer reliable gatekeepers.

This video clip is a "deepfake" of Kim Jong Un created in 2020 by RepresentUs, a non-partisan non-profit organization that produced it to raise public awareness about fake news in the emerging age of generative AI. The footage itself is fully synthetic, yet it’s an amalgamation from genuine video footage and voice samples of the North Korean leader drawn from publicly available news archives. It was intentionally designed to look real enough to unsettle us, but still recognizable as “fake” for educational purposes. Since 2020, of course, deepfake technology has advanced by orders of magnitude, in terms of realism, accessibility, and speed of production.

Evaluating Online Sources

Wed, 26 Mar 2025 20:28:08 +0000

1. OVERVIEW

This lesson will be focusing on understanding and evaluating evidence and information sources, a crucial aspect of constructing persuasive arguments. It explains how evidence interacts with values, and presents general tests for assessing the quality of evidence. We will also be learning how to locate and evaluate various sources of evidence, guiding you on choosing reliable information from books, periodicals, websites, and more. The chapter emphasizes the importance of digital literacy and critical evaluation of different types of sources.

Media Bias Chart published by Ad Fontes Media, 2020. Fact-checking always lags behind the emergence of new biased sources of information.

2. UNDERSTANDING EVIDENCE

Evidence and Values

In public discourse, evidence is invariably filtered through the "terminal screens" of societal norms and cultural values, leading to divergent interpretations even when presented with the same set of facts. Consider, for instance, debates surrounding Artificial Intelligence (AI) and employment. For techno-optimists, as represented by some Silicon Valley entrepreneurs, rapid technological advancements are seen as essential for societal evolution. They may interpret the emergence of Artificial General Intelligence (AGI) labor as an auspice for accelerated economic growth, productivity, and innovation, contending that AI liberates human workers from repetitive labor and allows greater engagement in creative, strategic, or emotionally rewarding tasks. Conversely, many labor advocates and trade unionists may interpret the prospect of an AGI workforce less positively. As critics of unchecked technological disruption, they might perceive this development as a harbinger of livelihood displacement, expressing concerns that automation could trigger widespread unemployment, diminish workers' bargaining power, and deepen existing economic inequalities. Such rhetorical divergence highlights how interpretations of evidence surrounding AI's impact are strategically framed to reinforce broader narratives of either progress or caution.