Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations

Abstract

In this paper we present a methodology to analyze and visualize streams of Social Media messages and apply it to a case in which Twitter is used as a backchannel, i.e. as a communication medium through which participants follow an event in the real world as it unfolds. Unlike other methods based on social networks or theories of information diffusion, we do not assume proximity or a pre-existing social structure to model content generation and diffusion by distributed users; instead we refer to concepts and theories from discourse psychology and conversational analysis to track online interaction and discover how people collectively make sense of novel events through micro-blogging. In particular, the proposed methodology extracts concept maps from twitter streams and uses a mix of sentiment and topological metrics computed over the extracted concept maps to build visual devices and display the conversational flow represented as a trajectory through time of automatically extracted topics. We evaluated the proposed method through data collected from the analysis of Twitter users’ reactions to the March 2015 Apple Keynote during which the company announced the official launch of several new products.