• Corpus ID: 208950408

Life in the network: The coming age of computational social science: Science

  title={Life in the network: The coming age of computational social science: Science},
  author={David Lazer and Alex Pentland and Anita Adami{\'c} and Sinan Aral and A L Barabasi and Devon Brewer and Nicholas A. Christakis and Noshir S. Contractor and James H. Fowler and Myron P. Gutmann and T. Hebara and Gary King and Michael W. Macy and Deb K. Roy and Marshall W. Van Alstyne},
Understanding social dynamics through big data
This thesis demonstrates that mobile phone data can provide reliable and dynamical estimates of population densities over large geographical extents, offering concrete solutions to population mapping issues in low-income countries and investigates the social mechanisms of success through the analysis of large-scale publication data.
From Raw Data to Social Systems - Separating the Signal from the Noise in Smartphone Sensor Measurements
The methods for location and interaction sensing that are proposed in the thesis constitute a more privacy-aware alternative to currently employed approaches and emphasize the fragility of the authors' privacy.
Communities of Communication: Making Sense of the “Social” in Social Media
Community detection, a set of methods for the discovery of closely knit groups, is presented as an intermediary step that enables application of existing traditional and network analytical approaches in a smaller setting more suited to social scientific questions.
Quantitative Verification of Social Media Networks: The Case Study of Twitter
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How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage
This work investigates the properties of learning and inferences of real world data collected via mobile phones for different sizes of analyzed networks, and proposes a method for advanced prediction of the maximal learning accuracy possible for the learning task at hand.
Measuring Large-Scale Social Networks with High Resolution
This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple
Sequences of purchases in credit card data reveal lifestyles in urban populations
This work defines a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior and cluster consumers by their credit card purchase sequences and discover five distinct groups, within which individuals also share similar mobility and demographic attributes.
Co-following on twitter
It is shown that co-following information provides strong signals for diverse classification tasks and that these signals persist even when the most discriminative features are removed.
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This research sheds light on how to analyze large-scale data within a virtual world by exploring the flow of trust in different layers of social networks with the help of Semantic Web technologies.
Sequence of purchases in credit card data reveal life styles in urban populations
This work applies text compression techniques to the purchase codes of credit card data to detect the significant sequences of transactions of each user and finds that properly deconstructing transaction data with Zipf-like distributions can give insights on collective behavior.