A computational linguistic analysis of the anatomy of production, consumption, and diffusion of misinformation and authentic information in social media: The case of the COVID-19 pandemic

Abstract

Social media has become a powerful conduit for misinformation during major public events. As a result, an extant body of research has emerged on misinformation and its diffusion. However, the research is fragmented and has mainly focused on understanding the content of misinformation messages. Little attention is paid to the production and consumption of misinformation. This study presents the results of a detailed comparative analysis of the production, consumption, and diffusion of misinformation with authentic information. Our findings, based on extensive use of computational linguistic analyses of COVID-19 pandemic-related messages on the Twitter platform, revealed that misinformation and authentic information exhibit very different characteristics in terms of their contents, production, diffusion, and their ultimate consumption. To support our study, we carefully selected a sample of 500 widely propagated messages confirmed by fact-checking websites as misinformation or authentic information about pandemic-related topics from the Twitter platform. Detailed computational linguistic analyses were performed on these messages and their replies ( N = 198,750). Additionally, we analyzed approximately 1.2 million Twitter user accounts responsible for producing, forwarding, or replying to these messages. Our extensive and detailed findings were used to develop and propose a theoretical framework for understanding the diffusion of misinformation on social media. Our study offers insights for social media platforms, researchers, policymakers, and online information consumers about how misinformation spreads over social media platforms.