‘The bot predicted rain, grab an umbrella’: few perceived differences in communication quality of a weather Twitterbot versus professional and amateur meteorologists

Abstract

Extending previous research on the human-to-human interaction script, the MAIN model, and the Computers Are Social Actors (CASA) paradigm, this study examines the influence of weather-related communication sources with varying levels of expertise and agency cues (i.e. human versus bot) on impressions of communication quality. Specifically, an experimental design was used to measure people’s impressions of source credibility, task and social attraction, computer-mediated communication competence, and intent to interact in the future for three information sources: a professional meteorologist, an amateur meteorologist, and a weather Twitterbot. Results demonstrated similar perceptions of communication quality for the three Twitter agents. However, the Twitterbot was rated as significantly less socially attractive than the professional meteorologist and as significantly more task attractive than the amateur meteorologist delivering the same messages. Agency-related cues appear to trigger heuristics that account for these differences in perception. Implications for the use of Twitterbots in weather-related and information-sharing contexts are discussed.