• Corpus ID: 208857555

Upscaling human activity data: an ecological perspective

  title={Upscaling human activity data: an ecological perspective},
  author={Anna Tovo and Samuele Stivanello and Amos Maritan and Samir Suweis and Stefano Favaro and Marco Formentin},
  journal={arXiv: Physics and Society},
In recent years we have witnessed an explosion of data collected for different human dynamics, from email communication to social networks activities. Extract useful information from these huge data sets represents a major challenge. In the last decades, statistical regularities has been widely observed in human activities and various models have been proposed. Here we move from modeling to inference and propose a statistical framework capable to predict global features of human activities from… 

Figures and Tables from this paper

Upscaling Statistical Patterns from Reduced Storage in Social and Life Science Big Datasets

This work strengthens an upscaling approach borrowed from theoretical ecology that allows us to infer with small errors relevant patterns of a dataset in its entirety, although only a limited fraction of it has been analysed.



SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

This paper builds on the theory of self-exciting point processes to develop a statistical model that allows for accurate predictions of the final number of reshares of a given post, and demonstrates a strong improvement in predictive accuracy over existing approaches.

Circadian Patterns of Wikipedia Editorial Activity: A Demographic Analysis

Using the cumulative data of 34 Wikipedias in different languages, the universalities and differences in temporal activity patterns of editors are characterized and the geographical distribution of editors for each WP in the globe is estimated.

A Poissonian explanation for heavy tails in e-mail communication

It is demonstrated that the approximate power-law scaling of the inter-event time distribution is a consequence of circadian and weekly cycles of human activity, and a cascading nonhomogeneous Poisson process is proposed that explicitly integrates these periodic patterns in activity with an individual's tendency to continue participating in an activity.

New activity pattern in human interactive dynamics

A new pattern for how the reactive dynamics of individuals is distributed across the set of each agent’s contacts is uncovered, uncovering a new behavioral pattern that might be universal, being present in other general interactive environments, and constrain future models of communication and interaction networks, affecting their architecture and evolution.

The dynamics of correlated novelties

A simple mathematical model is proposed that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs and predicts statistical laws for the rate at which novelties happen and for the probability distribution on the space explored.

Scaling identity connects human mobility and social interactions

By exploiting three different mobile phone datasets that capture simultaneously human movements and social interactions, a new scaling relationship is discovered, mediated by a universal flux distribution, which links the critical exponents characterizing the spatial dependencies in human mobility and social networks.

The origin of bursts and heavy tails in human dynamics

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.

Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

It is shown that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

Political Turbulence: How Social Media Shape Collective Action

As people spend increasing proportions of their daily lives using social media, such as Twitter and Facebook, they are being invited to support myriad political causes by sharing, liking, endorsing,

Optimal prediction of the number of unseen species

A class of simple algorithms are obtained that provably predict U all of the way up to t∝log⁡n samples, and it is shown that this range is the best possible and that the estimator’s mean-square error is near optimal for any t.