Predictability of conversation partners

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.

38 pages, 19 figures

References in corpus (37)

• Community structure in social and biological networks
• Community detection in graphs
• Power-law distributions in empirical data
• Understanding individual human mobility patterns
• Uncovering the overlapping community structure of complex networks in nature and society
• Assortative mixing in networks
• The architecture of complex weighted networks
• Hierarchical organization of modularity in metabolic networks
• Statistical physics of social dynamics
• The origin of bursts and heavy tails in human dynamics
• The scaling laws of human travel
• Hierarchical Organization in Complex Networks
• Structure and tie strengths in mobile communication networks
• Why social networks are different from other types of networks
• Modeling the scaling properties of human mobility
• Prediction and predictability of global epidemics: the role of the airline transportation network
• Intensity and coherence of motifs in weighted complex networks
• Dynamics of person-to-person interactions from distributed RFID sensor networks
• What's in a crowd? Analysis of face-to-face behavioral networks
• High resolution dynamical mapping of social interactions with active RFID
• Modeling bursts and heavy tails in human dynamics
• Small But Slow World: How Network Topology and Burstiness Slow Down Spreading
• Uncovering individual and collective human dynamics from mobile phone records
• Weighted Evolving Networks
• Information diffusion epidemics in social networks
• A Poissonian explanation for heavy-tails in e-mail communication
• A message passing approach for general epidemic models
• Curvature of Co-Links Uncovers Hidden Thematic Layers in the World Wide Web
• Impact of non-Poisson activity patterns on spreading processes
• Betweenness centrality correlation in social networks
• The dynamical strength of social ties in information spreading
• Small-world behavior in time-varying graphs
• Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors
• Scaling laws of human interaction activity
• Spreading Dynamics Following Bursty Human Activity Patterns
• Update rules and interevent time distributions: Slow ordering vs. no ordering in the Voter Model
• Voter model with non-Poissonian interevent intervals

Cited by in corpus (28)

• Temporal Networks
• Memory in network flows and its effects on spreading dynamics and community detection
• Representing higher-order dependencies in networks
• Bursty Human Dynamics
• Mapping higher-order network flows in memory and multilayer networks with Infomap
• Importance of individual events in temporal networks
• Entropy and the Predictability of Online Life
• On Predictability of Time Series
• Big data analyses reveal patterns and drivers of the movements of southern elephant seals
• Spatially distributed social complex networks
• Self-exciting point process modeling of conversation event sequences
• Entropy Measures of Human Communication Dynamics
• Generative models of simultaneously heavy-tailed distributions of inter-event times on nodes and edges
• Entropy-growth-based model of emotionally charged online dialogues
• Latent geometry and dynamics of proximity networks
• Bounds of memory strength for power-law series
• Universal bursty behavior in the air transportation system
• Lifetime distributions for adjacency relationships in a Vicsek model
• Inside the Echo Chamber: Disentangling network dynamics from polarization
• Entropy Rate Estimation for Markov Chains with Large State Space
• Metapopulation models imply non-Poissonian statistics of interevent times
• Dynamics of hot random hyperbolic graphs
• Dynamics of cold random hyperbolic graphs with link persistence
• Punctuated equilibrium dynamics in human communications
• Two Categories of Indoor Interactive Dynamics of a Large-scale Human Population in a WiFi covered university campus
• Measuring Contextual Relationships in Temporal Social Networks by Circle Link
• Universal features of correlated bursty behaviour
• Practical Prediction of Human Movements Across Device Types and Spatiotemporal Granularities