Analyzing Impact Dynamics of Misinformation Spread on X (Formerly Twitter) With a COVID-19 Dataset

Abstract

The spread of misinformation on social media platforms such as Twitter has significant societal implications, including influencing public opinion and causing trust issues with information sources. Our research addresses the critical question: How does misinformation propagate through Twitter, and what are the key factors influencing its spread and longevity? We conducted an extensive analysis of the dynamics of misinformation dissemination using an annotated collection of tweets. Our methodology integrates network science metrics and community detection algorithms to study influential accounts and analyze their impact on misinformation spread. We developed and implemented an algorithm that predicts the potential reach and longevity of tweets by considering account influence, network centrality, tweet readability, and multimedia presence. Our findings reveal that network structures, as well as influential accounts identified through centrality and popularity based metrics, significantly affect the dissemination and persistence of misinformation. The results from our impact analysis algorithm highlight the inclination of misinformation to spread more widely and persist longer than truthful information. This study provides a deeper understanding of the structural and content-related aspects of misinformation spread on Twitter, contributing valuable insights into combating the influence of misinformation on social media platforms.

Keywords

• Misinformation spread
• COVID-19
• data science
• large scale networks
• network analysis