Honest Signals (Petland, 2010, The MIT Press)
“For the fi rst time, we can precisely map the behavior of large numbers of people as they go about their normal lives. By using cell phones and electronic badges with integrated sensors, my students and I have observed hundreds of participants for periods of up to a year. In the process we amassed hundreds of thousands of hours of detailed, quantitative data about natural, day-to-day human behavior—far more data of these kind than have ever been available before. A new measurement tool such as this often brings with it a new understanding of what you are measuring. What we have found is that many types of human behavior can be reliably predicted from biologically based honest signaling behaviors. These ancient primate signaling mechanisms, such as the amount of synchrony, mimicry, activity, and emphasis, form an unconscious channel of communication between people—a channel almost unexplored except in other apes”
Marketing as surveillance (from Inside Marketing, 2012, ed. Zwick and Cayla)
“The consumer becomes “known” in the compartmentalization of their data, “assembled” in the production of a consumer “brand,” and all of this is made possible by forms of commercial sociology that operate as complex andeffective surveillance systems. This notion of commercial sociology describesthe kinds of analysis done by contemporary marketing companies. Customers are reduced to datasets, as information about purchasing preferences and proclivities is detailed to provide marketers with useful profiles. Such commercial sociology thus simulates consumers for analysis. But it also interprets these behaviors and normalizes them as a part of the surveillance process and, in addition, incorporates further information on the activities and preferences of consumers as feedback”
A Personal Perspective on the Origin(s) and Development of Big Data”: The Phenomenon, the Term, and the Discipline (Francis X. Diebold, 2012)
Abstract: I investigate Big Data, the phenomenon, the term, and the discipline, with emphasis on origins of the term, in industry and academics, in computer science and statistics/econometrics. Big Data the phenomenon continues unabated, Big Data the term is now rmly entrenched, and Big Data the discipline is emerging.
Understanding the paradigm shift to computational social science in presence of big data (Chang et alii, 2012)
“The era of big data has created exciting new opportunities for research to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of very large-scale data collection techniques and the related new technological support, there seem to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts include social networks and blogs, political discourse on the Internet, corporate announcements and digital journalism, mobile telephony and digital home entertainment, online gaming and social shopping, and social advertising and social commerce – and much more. The increasingly advantageous costs of data collection, and the new capabilities that researchers have to conduct research that leverages the spectrum of micro-, meso and macro-level data suggest the possibility of a scientific paradigm shift toward computational social science with big data”
A planetary nervous system for social mining and collective awareness (Giannotti et alii, 2012)
“We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis”
The aim of this special issue of the Fibreculture Journal is to address some of the contemporary challenges involved in working with affect across disciplines and practices that centre on the use of interactive- or digital technologies. The issue has a special focus on interaction design, interaction-based art and digital art. The pivotal question, as we see it, might be framed roughly like this: How do we explore the “field of questioning” that arises when we approach the affective in relation to interaction design, interaction-based art and digital art? What is the use of disciplinary goals when the affective has been proven most valuable in trans-disciplinary theory? Where do we go from here, that is, how can we continue working with the notion of affect, develop it in new theoretical, analytical and practical domains? What key concepts would emerge from this continued trajectory and how would they feed back onto the theoretical propositions? How would they resonate within and with-out existing disciplines, creating novel links and assemblages?
An introduction to social network data analytics (Aggarwal, 2012)
“The advent of online social networks has been one of the most exciting events in this decade. Many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular. In addition, a number of multimedia networks such as Flickr have also seen an increasing level of popularity in recent years. Many such social networks are extremely rich in content, and they typically contain a tremendous amount of content and linkage data which can be leveraged for analysis. The linkage data is essentially the graph structure of the social network and the communications between entities; whereas the content data contains the text, images and other multimedia data in the network. The richness of this network provides unprecedented opportunities for data analytics in the context of social networks. This book provides a data-centric view of online social networks; a topic which has been missing from much of the literature”
Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011)
The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work, we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns of people’s travels? How big attractors and extraordinary events in???uence mobility? How to predict areas of dense traf???c in the near future? How to characterize traf???c jams and congestions?
A framework for Big Data Governance (image by Big Data Governance, Sunil Soares, 2013)