Predictability of conversation partners

@article{Takaguchi2011PredictabilityOC,
  title={Predictability of conversation partners},
  author={Taro Takaguchi and Mitsuhiro Nakamura and Nobuo Sato and Kazuo Yano and Naoki Masuda},
  journal={ArXiv},
  year={2011},
  volume={abs/1104.5344}
}
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… 

Entropy and the Predictability of Online Life

TLDR
Observations show that predicting behavioral actions requires less information than predicting the mobility patterns of humans for which the additional knowledge of past visited locations is crucial and that the type and sign of a social relation has an essential impact on the ability to determine future behavior.

Importance of individual events in temporal networks

TLDR
It is found that the proposed measure properly quantifies the importance of events in connecting nodes along time-ordered paths and that an event is central when it carries new information about others to the two nodes involved in the event.

Human proximity networks: analysis, modeling and dynamical phenomena

TLDR
This thesis presents the statistical characterization of physical proximity dynamics, put into relation with the context and other available metadata such as the age, the gender of participants or the structure of their virtual social networks, suggesting simple microscopic interaction rules able to produce the complex macrostructure of interaction durations.

Predictability of action time for online human behaviors

  • Wang ChenQ. GaoHuagang Xiong
  • Computer Science
    2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)
  • 2016
TLDR
Results show that the action time of online activities in weekdays is more predictable than that in weekends, which indicates that the short and long inter-event times have higher predictive powers.

Temporal correlations in social multiplex networks

TLDR
It is shown that such temporal correlations do exist in social interactions where they act to depress the tendency to concentrate long stretches of activity on the same layer and imply some amount of potential predictability in the connection patterns between layers.

Two Categories of Indoor Interactive Dynamics of a Large-scale Human Population in a WiFi covered university campus

To explore large-scale population indoor interactions, we analyze 18,715 users' WiFi access logs recorded in a Chinese university campus during 3 months, and define two categories of human

Practical Prediction of Human Movements Across Device Types and Spatiotemporal Granularities

TLDR
Through analysis, the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations, is substantiated.

Temporal dynamics and impact of event interactions in cyber-social populations.

TLDR
This work defines event interaction (EI) to characterize the concurrent interactions of multiple users inferred by their geographic coincidences-co-locating in the same small region at the same time, and proposes three rules to construct a transmission graph, which depicts the topological and temporal features of event interactions.

Self-exciting point process modeling of conversation event sequences

TLDR
This work examines some basic properties of event sequences generated by the Hawkes self-exciting process to show that it generates bursty interevent times for a wide parameter range and fits the model to the data of conversation sequences recorded in company offices in Japan.
...

References

SHOWING 1-10 OF 48 REFERENCES

Limits of Predictability in Human Mobility

TLDR
Analysis of the trajectories of people carrying cell phones reveals that human mobility patterns are highly predictable, and a remarkable lack of variability in predictability is found, which is largely independent of the distance users cover on a regular basis.

Evidence for a bimodal distribution in human communication

TLDR
This work presents clear empirical evidence from Short Message correspondence that observed human actions are the result of the interplay of three basic ingredients: Poisson initiation of tasks and decision making for task execution in individual humans as well as interaction among individuals.

Uncovering individual and collective human dynamics from mobile phone records

TLDR
The mean collective behavior at large scales is studied and it is shown that the interevent time of consecutive calls is heavy-tailed, which has implications for dynamics of spreading phenomena in social networks.

Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks

TLDR
A scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities is presented and shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections.

Modeling bursts and heavy tails in human dynamics

TLDR
It is shown that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times.

Scaling laws of human interaction activity

TLDR
This research identifies a generalized version of Gibrat's law of social activity expressed as a scaling law between the fluctuations in the number of messages sent by members and their level of activity and attributes this scaling law to long-term correlation patterns in human activity.

The origin of bursts and heavy tails in human dynamics

TLDR
It is shown that the bursty nature of human behaviour is a consequence of a decision-based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, with most tasks being rapidly executed, whereas a few experience very long waiting times.

Understanding individual human mobility patterns

TLDR
The trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period is studied, finding that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity.

Structure and tie strengths in mobile communication networks

TLDR
It is found that, when it comes to information diffusion, weak and strong ties are both simultaneously ineffective, and this coupling significantly slows the diffusion process, resulting in dynamic trapping of information in communities.