Beyond the Power of Mere Repetition: Forms of Social Communication on Twitter through the Lens of Information Flows and Its Effect on Topic Evolution

Abstract

Understanding how people interact and exchange messages on social networks is significant for managing online contents and making predictions of future behaviors. Most existing research on the communication characteristics simply focuses on the user involvement. The current work largely neglects the content changes that imply how wide and deep the discussion in a topic goes, and to what degree people set forth their own views with the additional information supplemented. We are highly motivated to propose a theoretical framework to target those issues. In this paper, we define the communication modality constructs, and classify topics based on three dimensions: user involvement, information flow depth, and topic inter-relations, which substantially extend the traditional focus in user interaction analysis. The communication modality constructs comprise of (i) topic dialogicity, (ii) discussion intensiveness, and (iii) discussion extensibility. We introduce a quantitative model based on the topology of information flow graph, and use the information addition as well as the emotion attachment along the path to measure the pattern divergence between topic groups. Our model is empirically validated by using 78 million tweets, and experiments on Twitter demonstrate our contributions.