epl draft Role of activity in human dynamics

Scientific Reports, 2013

Human behaviors are often driven by human interests. Despite intense recent efforts in exploring the dynamics of human behaviors, little is known about human-interest dynamics, partly due to the extreme difficulty in accessing the human mind from observations. However, the availability of large-scale data, such as those from e-commerce and smartphone communications, makes it possible to probe into and quantify the dynamics of human interest. Using three prototypical "big data" sets, we investigate the scaling behaviors associated with human-interest dynamics. In particular, from the data sets we uncover power-law scaling associated with the three basic quantities: (1) the length of continuous interest, (2) the return time of visiting certain interest, and (3) interest ranking and transition. We argue that there are three basic ingredients underlying human-interest dynamics: preferential return to previously visited interests, inertial effect, and exploration of new interests. We develop a biased random-walk model, incorporating the three ingredients, to account for the observed power-law scaling relations. Our study represents the first attempt to understand the dynamical processes underlying human interest, which has significant applications in science and engineering, commerce, as well as defense, in terms of specific tasks such as recommendation and human-behavior prediction. A fundamental feature of a human society is that its individuals possess all kinds of interests, the driving force of many human behaviors. Some interests may last for a lifetime while others can fade away in short time. From time to time our interests also change. In the modern society that we live in, all kinds of attractions and temptations emerge and disappear on a daily basis. Does this mean that the evolution of our interest is mostly random? Or are there intrinsic dynamical rules that govern how human interests evolve with time? To answer these questions was deemed to be extremely difficult, due to the lack of appropriate means to characterize human mind and to measure quantitatively how it changes with time. Yet the questions are fundamental in science, and any revelation of the dynamics of human interest may have significant applications in commerce, medical sciences, and even defense. In particular, in commerce, adequate knowledge of customer interests and how they change with time are key to the success of many businesses as such knowledge can be of tremendous value to advertisement design and product promotion. In psychiatry, a good understanding of patients' interests may help generate accurate diagnosis and devise effective therapeutic approaches. In defense, timely and reliable assessment of certain group or individuals' interests and their time evolution can help predict the group or individuals' possible future behaviors and actions. Apparently, all these rely on human-interest dynamics' being not completely random. There have been efforts in modeling and understanding human behaviors that are essential to many social and economical phenomena, with significant applications in areas ranging from resource allocation and transportation control to epidemic prediction and personal recommendation [1-4]. The pursuit has been facilitated greatly by the advances in information technology, especially by the availability of massive Internet data and resources [5]. However, to probe into human-interest dynamics is more challenging, due to the difficulty in characterizing human interests and traditional lack of data sets from which the underlying dynamical processes may be deduced. In recent years "big data" sets, such as those from e-commerce or mobile-phone communications, become commonly available, making it possible to quantify human interests and to infer their intrinsic dynamics. As a branch of the science of "Big Data," the field of human-interest dynamics is at its infancy. A viable approach to probing into human-interest dynamics is to use data analysis as a getaway to uncover various phenomena and possible scaling laws. Guided by this principle, in this paper we explore two e-commerce data sets (Douban, Taobao) and one communication data set [Mobile-Phone Reading (MPR)], and focus on three issues: statistical distribution of the time that an interest lasts, distribution of the return time to revisit a particular interest, and interest ranking and transition. Considering the large number of factors that can affect human interest, such as the specific activity contents and distractions of the individual's attention, it seems plausible that the underlying dynamics be completely random [6-8]. Indeed, a widely used assumption is that of the Markovian type of dynamics for individuals' online behaviors, in which an online user's next action depends not on his/her history of interests but on the current interest only [9-11]. However, there is recent evidence [12, 13] of deviations from the Markovian dynamics. Our systematic analysis of the three data sets reveals an unequivocal signature of the power-law scaling behavior characteristic of non-equilibrium complex systems and, consequently, indicates the existence of intrinsic dynamical rules governing the human-interest dynamics. Based on the empirical analysis, we identify three basic ingredients underlying the dynamics: preferential return, inertia effect and exploration. A mathematical model incorporating these ingredients is then

2012

Human communication in social networks is dominated by emergent statistical laws such as non-trivial correlations and temporal clustering. Recently, we found long-term correlations in the user's activity in social communities. Here, we extend this work to study the collective behavior of the whole community with the goal of understanding the origin of clustering and long-term persistence. At the individual level, we find that the correlations in activity are a byproduct of the clustering expressed in the power-law distribution of inter-event times of single users, i.e. short periods of many events are separated by long periods of no events. On the contrary, the activity of the whole community presents long-term correlations that are a true emergent property of the system, i.e. they are not related to the distribution of inter-event times. This result suggests the existence of collective behavior, possibly arising from nontrivial communication patterns through the embedding social network.

2007

Abstract: The many-to-many social communication activity on the popular technology-news website Slashdot has been studied. We have concentrated on the dynamics of message production without considering semantic relations and have found regular temporal patterns in the reaction time of the community to a news-post as well as in single user behavior. The statistics of these activities follow log-normal distributions. Daily and weekly oscillatory cycles, which cause slight variations of this simple behavior, are identified.

Physical Review E, 2006

The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behavior into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. Here we provide direct evidence that for five human activity patterns, such as email and letter based communications, web browsing, library visits and stock trading, the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. We show 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. In contrast, priority blind execution is well approximated by uniform interevent statistics. We discuss two queueing models that capture human activity. The first model assumes that there are no limitations on the number of tasks an individual can hadle at any time, predicting that the waiting time of the individual tasks follow a heavy tailed distribution P (τw) ∼ τ −α w with α = 3/2. The second model imposes limitations on the queue length, resulting in a heavy tailed waiting time distribution characterized by α = 1. We provide empirical evidence supporting the relevance of these two models to human activity patterns, showing that while emails, web browsing and library visitation display α = 1, the surface mail based communication belongs to the α = 3/2 universality class. Finally, we discuss possible extension of the proposed queueing models and outline some future challenges in exploring the statistical mechanisms of human dynamics. These findings have important implications not only for our quantitative understanding of human activity patterns, but also for resource management and service allocation in both communications and retail.

Modern technologies not only provide a variety of communication modes (e.g., texting, cell phone conversation, and online instant messaging), but also detailed electronic traces of these communications between individuals. These electronic traces indicate that the interactions occur in temporal bursts. Here, we study intercall duration of communications of the 100,000 most active cell phone users of a Chinese mobile phone operator. We confirm that the intercall durations follow a power-law distribution with an exponential cutoff at the population level but find differences when focusing on individual users. We apply statistical tests at the individual level and find that the intercall durations follow a power-law distribution for only 3,460 individuals (3.46%). The intercall durations for the majority (73.34%) follow a Weibull distribution. We quantify individual users using three measures: out-degree, percentage of outgoing calls, and communication diversity. We find that the cell phone users with a power-law duration distribution fall into three anomalous clusters: robot-based callers, telecom fraud, and telephone sales. This information is of interest to both academics and practitioners, mobile telecom operators in particular. In contrast, the individual users with a Weibull duration distribution form the fourth cluster of ordinary cell phone users. We also discover more information about the calling patterns of these four clusters (e.g., the probability that a user will call the c r -th most contact and the probability distribution of burst sizes). Our findings may enable a more detailed analysis of the huge body of data contained in the logs of massive users.

Temporal social networks are characterized by heterogeneous duration of contacts which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics which explains the data observed in these different types of social interaction. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.

Mathematical Problems in Engineering, 2012

Statistical properties of the human comment behavior are studied using data from "Tianya" and "Tieba" which are very popular online social systems or forums in China. We find that both the reply number R and the view number V of a thread in a subforum obey the power-law distributions P R R α and P V ∝ V β , respectively, which indicates that there exists a kind of highly popular topics. These topics should be specially paid much attention, because they play an important role in the public opinion formation and the public opinion control. In addition, the relationship between R and V also obeys the power-law function R ∝ V γ . Based on the human comment habit, a model is introduced to explain the human view and reply behaviors in the forum. Numerical simulations of the model fit well with the empirical results. Our findings are helpful for discovering collective patterns of human behaviors and the evolution of public opinions on the virtual society as well as the real one.

Arxiv preprint arXiv:1104.0742, 2011

Advances in Physics: X

In the post year 2000 era the technologies that facilitate human communication have rapidly multiplied. While the adoption of these technologies has hugely impacted the behaviour and sociality of people, specifically in urban but also in rural environments, their 'digital footprints' on different data bases have become an active area of research. The existence and accessibility of such large population-level datasets, has allowed scientists to study and model innate human tendencies and social patterns in an unprecedented way that complements traditional research approaches like questionnaire studies. In this review, we focus on data analytics and modelling researchalong the lines of Social Physicsas it has been carried out using the mobile phone datasets to get insight into the various aspects of human sociality, burstiness in communication, mobility patterns, and daily rhythms.