Monday, 14 November 2016

Introducing the Critical Data Studies Special Theme

by Andrew Iliadis (University of Ontario Institute of Technology) and Federica Russo (University of Amsterdam)

Big Data science, along with its methodologies and practices, has reshaped the landscape of the natural and social sciences. Much has been written about the benefits of Big Data’s contributions to advancing research, training, and encouraging engagement at the intersection of computation and society. Much less has been said about the existing and potential harms caused by Big Data. As the product of multiple sites of work, layered analytic techniques, experimental practices, and various competing discourses, Big Data must remain open to cultural, ethical, and critical challenges.

The Big Data & Society Critical Data Studies (CDS) special theme brings together established and emerging CDS researchers who seek a critical engagement with Big Data in various contexts, including food and agriculture, policing and governance, finance, environmental regulation, philosophy, statistics, epidemiology, and geography. Each of the articles focus on what Rob Kitchin has called “data assemblages”—apparatuses that contribute to or generate Big Data science, including systems of thought, forms of knowledge, finance, political economy, governmentalities and legalities, materialities and infrastructures, practices, organizations and institutions, subjectivities and communities, places, and the marketplace where data are constituted.

This project grew out of the Society for the Philosophy of Information’s Seventh Workshop, “Conceptual Challenges of Data in Science and Technology” (2015, University College London). 

Monday, 7 November 2016

Editor Evelyn Ruppert keynote speaker at Big Data in Asian Society workshop in Singapore

Evelyn joined researchers at Nanyang Technological University in Singapore from 27-28 October to discuss the issues, challenges and meanings of Big Data in Asian societies. She delivered a keynote on 'Data Politics' and participated in discussions with researchers from India, Taiwan, Hong Kong, Singapore, and United States.

Saturday, 5 November 2016

Introducing special issue on Spatial Big Data

by Ate Poorthuis

In our most recent special issue Agnieszka Leszczynski and Jeremy Crampton have drawn together an engaging series of articles on Spatial Big Data and Everyday Life. The theme explores what it means to encounter, experience and study the spatial dimension of big data. The issue looks at what it actually means to explicitly think about spatial big data and then examines what the effects of this type of data are on the everyday lives of people.

In their introductory article, Leszczynski and Crampton make clear that ‘spatial’ should be thought of as more than just the geographical reference itself (e.g. coordinates or addresses) that plays such a dominant role in (geographic) academic research. Instead, they call for work that goes beyond both this fixation on the geotag – on the precise location on the Earth’s surface where a data point happens to be produced, as well as the emphasis on only social media content. The contributions to the special issue do exactly this – from a wide variety of angles.

For example, Alvarez Leon analyzes how geographic information is commodified through the use of technology (e.g. APIs). He illustrates this by looking at how Google Street View produces a series of copyrighted digital landscapes, merged together in a virtual navigable environment and discusses how the issue of individual privacy is intertwined within this process. Cockayne’s contribution switches from economic value to the affective value of Big Data, connecting to recent discourses on the attention economy. As he argues, Bay Area startups and tech firms not only produce economic value but are also effective at capturing user attention (i.e. affective value). Going beyond economic analysis and studying the systems at work in this attention economy, will help further our understanding of the many dimensions of Big Data production.

Expanding the notion of big data beyond the singular further, Straube uses the analogy of the stack, borrowed from information technology itself, to give us a useful framework to think through the many layers, technologies and actors involved in Big Data. Also exploring the multiplicity of Big Data is Wilmott’s contribution, which uses ethnographic walking interviews in Sydney and Hong Kong to analyze the quotidian, lived experience of spatial big data. Interestingly, she shows that Big Data is not the totalizing process it is often portrayed as, but is just as often incomplete, erroneous or simply missing. Finally, Thatcher reminds us that the data points – objects – in many Big Data studies are ultimately produced by people and thus warrant attention to subject positionality. To bring back this understanding, he argues for ‘a reseating of the reflexive, self-eliciting subject’ in research with and on spatial big data.

Tuesday, 13 September 2016

Highlights from the 2016 #SMSociety - International Conference on Social Media & Society



After a year of planning and preparations, it’s hard to believe that the 2016 International Conference on Social Media & Society is now officially part of the history. But what amazing three days they were. Now, in its 7th year, the 2016 Conference was held from July 11-13 at Goldsmiths, University of London, UK. The conference was organized by the Social Media Lab at Ryerson University (Canada) and co-hosted by the Big Data & Society Journal (BD&S) along with the Centre for Creative & Social Technologies (CAST) at Goldsmiths, University of London.

Thank you to all Volunteers, Attendees, Presenters, Program Committee, Partners, our keynotes, Dr. Susan Halford (Web Science Institute, University of Southampton, UK) and Dr. Helen Kennedy (University of Sheffield, UK), and everyone who made this year's conference a huge success! Let’s do it again! Mark your calendar, the 2017 International Conference on Society Media & Society will be in Toronto on July 28-30, 2017. (CfP will be released later this fall.)


In case you missed the conference this year (or just want to relive some highlights), here is a list of online resources for you:
Also not to be missed, here is a list of blog posts by the conference attendees:

~2016 #SMSociety Organizing Committee
Anatoliy Gruzd, Philip Mai, Marc Esteve Del Valle, Ryerson University, Canada;
Jenna Jacobson, University of Toronto, Canada; 
Evelyn Ruppert, Dhiraj Murthy, Ville Takala, Goldsmiths, University of London, UK;

Friday, 15 July 2016

Online health and fitness apps in a platform society - José Van Dijck and Thomas Poell

by José Van Dijck and Thomas Poell

Over the past few years, hundreds of thousands of health and fitness apps have flooded the internet. What are the promises these apps make and what premises are they based on? Many apps promise to offer personal solutions to medical problems while also contributing to the public good. Online platforms serve as personalized data-driven services to their customers. At the same time, they allegedly serve public interests, such as medical research or health education. In doing so, health and fitness apps often employ a diffuse discourse, hinging on terms like ‘‘sharing,’’ ‘‘open,’’ and ‘‘reuse’’ when they talk about data extraction and distribution.

Through three examples (23andme, PatiensLikeMe and Parkinson mPower), our recent article in Big Data & Society "Understanding the promises and premises of online health platforms" traces how the mechanisms of datafication and commodification introduce a new dynamic in health care and health research. In this domain, datafication means that every aspect of one’s physical or mental well-being is translated into data— vital signs, objective measurements, subjective experiences, medicine intake, personal information, test results, etc. Data can be private and personal (e.g., recorded symptoms, experiences) or they can be public and collective (e.g., clinical research data, health demographics, statistics); data can be user-generated and reported automatically through devices, such as electronic heartbeat apps, or users themselves can contribute data consciously, for instance through deploying pedometers.  What kinds of (user) data do platforms collect, how do they collect them, and how do they process and reuse those data?

These kind of questions are important when we try to analyse how datafied information is transformed into (monetary) value. Some platforms sell health information products to customers, sometimes in combination with advertisements; other apps are free to users in exchange for their personal data, which may be shared with paying co-patients and most important industrial partners. Virtually all platforms collaborate with such partners: high-tech firms and pharmaceutical or medical equipment companies. Some also partner with universities, government services, or a combination thereof, mixing for profit and nonprofit. A minority of health platforms is operated via government or nonprofit organizations, intent on pursuing public values and yielding public goods. The question is which business model is used for what purposes, who owns and operates the platform, and who gets to benefit from its products?

We conclude the article by connecting these individual examples to the wider implications of health apps’ data flows, governance policies, and business models. Regulatory bodies tend to focus on the (medical) safety and security of apps, but pay scarce attention to health apps’ techno-economic governance. It is important to look beyond the utilitarian regulatory scope that most governments are currently envisioning and understand the technical and social dynamics underpinning the ecosystem. Who owns user-generated health data and who gets to benefit? Whereas legislators are commonly called upon to define ontological and normative standards, their power seems weakened in the face of an emerging global ecosystem of online platforms, whose techno-economic dynamics appear to operate autonomously. Hence, it takes the concerted efforts of not only governments, but also citizens, responsible scientists, and entrepreneurs to secure the checks and balances in the organization of health care in a future platform society.

This article is part of a larger research project called The Platform Society. In this project, we critically examine how online platforms—ranging from MOOCs to health apps, and from social media to sharing economy platforms—penetrate all kinds of sectors of public life such as education, health care, journalism, and civic engagement. The project’s starting point is the question: what role do platforms play in the development and realization of key public values? Our research shows that the mutual articulation of technologies, economies, and practices produces three powerful mechanisms –datafication, commodification, and selection- that reshape how societal organizations operate and how public value is produced.

About the authors:


José van Dijck 

José van Dijck is a professor of Comparative Media Studies at the University of Amsterdam. Her work covers a wide range of topics in media theory, media technologies, social media, television and culture. For more information see:
medewerker.uva.nl/j.f.t.m.vandijck/

Thomas Poell

Thomas Poell is an assistant professor of New Media and Digital Culture at the Department of Media Studies at the University of Amsterdam. His research focuses on social media and the transformation of public communication around the globe. See: medewerker.uva.nl/t.poell/

Monday, 23 May 2016

Dave Beer introduces his new article "How should we do the history of Big Data?"

In this video abstract, Dave Beer, Reader in Sociology at the University of York, introduces his new article in Big Data & Society "How should we do the history of Big Data?

Monday, 18 April 2016

Book by BD&S Author Dennis Mazur: Science in Medicine (2015)


by Dennis Mazur
Scientific data can be used in medicine or it can be ignored by physicians and others. I began my search of the use of scientific evidence based on the analysis of large data sets for the state in the work of John Graunt in the 1600s and in the work on developing and analyzing large data sets to prove the efficacy of a surgical procedure, lithotrity, by French urologist, Jean Civale, in the 1800s.  Two commentaries on these examples were published in Big Data & SocietyBig Data in the 1800s in surgical science and Analyzing and interpreting “imperfect” Big Data in the 1600s.

The commentaries are related to my book, Science in Medicine: From Authoritative Opinion through Evidence-Based Medicine to Big Data and Beyond, which examines the history of the development of science in medicine from its origins in authoritative opinion through evidence-based medicine to Big Data today and beyond. I examine what was needed in medicine before there was an acceptance of the importance of the construction and examination of large data sets. Firstly, I examine what shifted some physicians’ views toward acceptance of the construction and analysis of large data sets as important understand how to best treat their patients and secondly, what obstacles remain in the minds of other physicians who still remain reluctant to recognize the importance of well-constructed and well-analyzed large data sets in the care of their patients. 

About the author
Dr. Mazur has served as Senior Scholar, Center for Ethics in Health Care, Oregon Health and Science University (OHSU), Portland, Oregon, USA; has served as Professor of Medicine, OHSU; and had served as Feature Editor of the journal Medical Decision Making.