The Impact of Language on Retweeting during Acute Crises: Uncertainty Reduction and Language Expectancy Perspectives

Abstract

Retweeting or resharing nature has made Twitter a popular medium of information dissemination during disasters, yet few studies have investigated the effect of the language on resharing behavior during acute emergencies. In this study, we develop, drawing upon language expectancy and uncertainty reduction theories as an enabling framework, hypotheses about how the language (i.e., style and content) influence resharing behavior. We employ a natural language processing of disaster tweets to examine how the language – linguistic style (concrete and interactive language) and linguistic content (information- and affect-focused language) – affects resharing behavior on Twitter during natural disasters. Our analysis of tweets from the 2013 Colorado floods shows that resharing disasters tweets increases with the use of concrete language style during acute emergencies. Interactive language is also positively associated with retweet frequency. In addition, neither positive nor negative emotional tweets have a significant effect on resharing during acute crises, while information-focused language content has a significantly positive effect on virality.

Keywords: Disaster Tweet, Information Diffusion, Linguistic Style, Uncertainty Reduction, Language Expectancy Theory