New activity pattern in human interactive dynamics

Abstract

We investigate the response function of human agents as demonstrated by written correspondence, uncovering a new universal pattern for how the reactive dynamics of individuals is distributed across the set of each agent's contacts. In long-term empirical data on email, we find that the set of response times considered separately for the messages to each different correspondent of a given writer, generate a family of heavy-tailed distributions, which have largely the same features for all agents, and whose characteristic times grow exponentially with the rank of each correspondent. We furthermore show that this universal behavioral pattern emerges robustly by considering weighted moving averages of the priorityconditioned response-time probabilities generated by a basic prioritization model. Our findings clarify how the range of priorities in the inputs from one's environment underpin and shape the dynamics of agents embedded in a net of reactive relations. These newly revealed activity patterns might be present in other general interactive environments, and constrain future models of communication and interaction networks, affecting their architecture and evolution.

Bursty Human Dynamics

“…In these cases, a close relation between P(τ) and P(τ w ) seems to appear. Actually, it has been argued that in case of a process with heterogeneous waiting time distribution, the interevent time distribution is also heterogeneous and vice versa, and can be characterised by the same exponent [21,288,177,70]. Waiting times will be duly addressed later in Section 4.1.1, where they appear as the central quantity in the definition of priority queuing models [2,21].…”

Temporal pattern of online communication spike trains in spreading a scientific rumor: how often, who interacts with whom?

“…Diffusion on a temporal network cannot be accurately described by models on static networks and consequently the process presents non-Markovian features with strong influence on the time required to explore the system [25,26]. Furthermore, the dynamics drives a strong heterogeneity observed in user activity [27,28] and user/content popularity [29][30][31]. Specifically, in Twitter, the heterogeneity in popularity has been observed and quantified in different ways by the size of retweet cascades, i.e., users re-transfer messages to their own followers with or without modifying them [32][33][34][35][36] or by the number of mentions of a user name, identified by the symbol @, in other people's tweets [37].…”

Upscaling human activity data: A statistical ecology approach

“…In the present work we focus on inference, not modeling. In particular, we consider four human activities with the following correspondence between species and individuals within each dataset (see Fig 1): (1) Email communication [35,36]: here we set the sender identity to label a species and the number of sent emails to be the number of individuals pertaining to a species; (2) Twitter posts [37]: here hashtags play the role of species and the number of different tweets containing a certain hashtag represents its population size; (3) For Wikipedia articles [37] and (4) Gutenberg books [37] we use the following setting: each word is a different species while its abundance is given by the number of occurrences of the word in the dataset. Once defined what corresponds to species and individuals, the RSA of each dataset displays a negative binomial behavior (see S1.1 Section in S1 Appendix).…”