Interaction Ritual Threads: Conversational Persistence in an Online Discussion at a Large Corporation (Social Science Matters - The Future Series)
Since the publication of Randall Collins's Interaction Ritual Chains, much interaction has moved from face-to-face to online settings. IRC theory was based on the former, and Collins has expressed a combination of skepticism and ambivalence as to the theory’s applicability to online interaction. This paper draws on Bakhtin’s theory of speech genres to adapt key concepts from IRC theory to the online world. Using more than 40,000 postings from two time-delimited intranet discussions at a global corporation, we find that IRC theory is effective in predicting which posts contribute to robust conversations by eliciting responses. As in face-to-face interaction rituals, shared topical focus contributes to success, and interactions are characterized by temporal rhythms similar to, but longer than, those in face to face talk. We conclude that under the right conditions, online interaction can produce emotional energy and that IRC theory is a valuable resource in understanding communication online.
Paul DiMaggio, Professor of Sociology at NYU, studies the formal and informal organization, the sociology of economic markets, social implications of information technology, and theory and methods in the sociology of culture. Recent papers have addressed the impact of network externalities on social inequality, the effects of Internet use on wages, applications of topic models to the study of culture, and the emergence of cultural hierarchy in 19th-century Chicago. Recent books include The Twentieth-First Century Firm: Changing Economic Organization in International Perspective (edited), Art in the Lives of Immigrant Communities in the U.S. (edited with Patricia Fernandez-Kelly), and Organizzare la cultura: Imprenditoria, istitutzioni e beni culturali. Current projects include analyses of heterogeneity in opinion data (with application to economic attitudes and nationalist sentiments in the U.S.; applications of machine-learning methods and sentiment analysis to the measurement of cultural change in societies and in formal organizations; and research on the impact of social networks on social inequality.