Mapping Information Flows on Twitter

Abstract

Social network services like Twitter and Facebook have created an expectation that you interact with your customers, followers and friends. There’s an expectation to connect rather than broadcast, listen and engage in conversations. But how can we expect to interact with our invisible audience when we can’t really see whose there? For the first time in history, there is a plethora of information produced by people’s actions. We can now observe a friend take another’s recommendation to purchase an item, or a powerful stream of clicks to content that we choose to curate. Social media professionals are jumping on the bandwagon and attempting to quantify social interactions by using terms like influence, reach, trust and klout. But even though data is more visible than ever, it is still representative of people’s complex reasoning mechanism, changing relationships, timing and logic. This paper looks at two different ways to analyze and display characteristics of online audiences on Twitter through information flows. By visualizing flows, it is possible to “put a face” to an audience, seeing interactions between interconnected users. By replaying a representation of the series of events, it is possible to note key moments in the act of information dissemination.