This book argues that misinformation poses a multi-faceted threat to knowledge, while arguing that some forms of content moderation risk exacerbating these threats. It proposes alternative forms of content moderation that aim to address this complexity while enhancing human epistemic agency. The proliferation of fake news, false conspiracy theories, and other forms of misinformation on the internet and especially social media is widely recognized as a threat to individual knowledge and, consequently, to collective deliberation and democracy itself. This book argues (...) that misinformation presents a three-pronged threat to knowledge. While researchers often focus on the role of misinformation in causing false beliefs, this deceptive potential of misinformation exists alongside the potential to suppress trust and to distort the perception of evidence. Recognizing the multi-faceted nature of this threat is essential to the development of effective measures to mitigate the harms associated with misinformation. The book weaves together work in analytic epistemology with emerging empirical work in other disciplines to offer novel insights into the threats posed by misinformation. Additionally, it breaks new ground by systematically assessing different forms of content moderation from the perspective of epistemology. Misinformation, Content Moderation, and Epistemology will appeal to philosophers working in applied and social epistemology, as well as scholars and advanced students in disciplines such as communication studies, political science, and social psychology who are researching misinformation. (shrink)

Judgments of misinformation are made relative to the informational preferences of the communities making them. However, informational standards change over time, inducing distribution shifts that threaten the adequacy of machine learning models of misinformation. After articulating five kinds of distribution shifts, three solutions for enhancing success are discussed: larger static training sets, social engineering, and dynamic sampling. I argue that given the idiosyncratic ontology of misinformation, the first option is inadequate, the second is unethical, and thus the third is superior. (...) However, I conclude that the prospects for machine learning models of misinformation are far weaker than most have presupposed, given that both epistemic and non-epistemic values are difficult to operationalize dynamically in machine code, rendering them surprisingly at most a species of recommender systems rather than literal truth detectors. (shrink)

Cognitive Science, Volume 46, Issue 10, October 2022.