Cascading falsehoods: mapping the diffusion of misinformation in algorithmic environments

Abstract In today’s AI-driven landscape, misinformation is not just a nuisance—it is reshaping how people understand the world, influencing conversations across social platforms and public life. This study draws on Rogers’ Diffusion of Innovation Theory to examine the dissemination of misinformation through the lens of adopter categories, diffusion attributes, and the classic diffusion curve. It draws from both human psychology and algorithmic logic to uncover why certain falsehoods catch on—especially when they align with what people already believe, stir strong emotions, or get boosted by platform algorithms. The study maps the trajectory of misinformation across four phases—introduction, acceleration, saturation, and stabilization—and classifies adopters into distinct categories reflective of their engagement with false content. This framework offers a nuanced understanding of how misinformation diffuses within both social and algorithmically mediated networks. This study builds on diffusion models by including how emotions and platform algorithms drive misinformation, helping us better understand how it spreads in today’s digital world. The findings yield actionable implications for policymakers, platform designers, and educators seeking to curb the spread of false information.