The Linguistic and Message Features Driving Information Diffusion on Twitter: The Case of #RevolutionNow in Nigeria

Abstract

Employing the diffusion of innovations theory, this study investigates how linguistic and message features of tweets drive information diffusion on Twitter in the case of #RevolutionNow, a 2019 Nigerian political activism event. Information diffusion was studied in terms of the number of favorites, replies, and retweets. Linguistic Inquiry and Word Count and inferential statistical analyses revealed that word choice, otherwise called linguistic categories (e.g., work, quantifiers), increased the diffusion of #RevolutionNow. Surprisingly, lengthy messages were found to be mostly positively correlated with the diffusion of tweets, whereas mentions and URLs mostly impeded favorites, replies, and retweets. Implications of these findings for innovation attributes (e.g., relative advantage, compatibility, complexity) and the diffusion of political activism on Twitter are discussed.