Abstract
Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information theoretic method to the spatiotemporal data of cell-phone locations, [C. Song et al. Science 327, 1018 (2010)] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one's conversation partners is defined as the degree to which one's next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to some extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by long-tailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual's predictability is correlated with the position in the static social network derived from the data. Individuals confined in a community - in the sense of an abundance of surrounding triangles - tend to have low predictability, and those bridging different communities tend to have high predictability.
Highlights
Interest in the statistical and dynamical features of human social behavior has been growing, enabled by the development of new devices that allow tracking of social data in real time, with increasing precision and duration [1,2,3,4,5,6,7,8,9]
We quantify the predictability of the partner sequence by the mutual information as follows: our primary interest in this study is the temporal properties of partner sequences, we analyze the conversation networks (CNs) G1 and G2 constructed by aggregating all the conversation events in D1 and D2, respectively, over the entire recording
We found that both CNs, G1 and G2, are composed of a single connected component
Summary
Interest in the statistical and dynamical features of human social behavior has been growing, enabled by the development of new devices that allow tracking of social data in real time, with increasing precision and duration [1,2,3,4,5,6,7,8,9]. Conversation events mediate the spreading and routing of diverse contents such as new ideas, opinions, and infectious diseases in social networks [15,16]. In models describing these phenomena, it is a norm that each individual possesses a dynamically changing state (e.g., opinion A or opinion B in opinion dynamics, and susceptible or infected state in epidemic dynamics). In contrast to conventional models in which the Poisson interval distribution is assumed, these results indicate that the conversation time, given the previous one, is relatively predictable in that a conversation event in the recent past is a precursor to a burst of events in the near future. Individuals that connect different communities by weak links tend to have a high predictability
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