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:

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:

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.

Monday, 8 February 2016

Introducing the Social Media & Society Special Theme

With faster computers and cheaper storage, bigger data sets are becoming abundant. Social media is a key source of big data in the form of user and system generated content and interactions. What do we do with all of the social data and how do we make sense of it? How does the use of social media platforms and the data that they generate change us, our organizations, and our society? What are the inherent challenges and issues associated with working with social media data? These are some of the questions we set out to answer in a new special theme on Social Media & Society in Big Data & Society (BD&S), an open access journal published by SAGE.

This special theme is built around research presented at the International Conference on Social Media & Society (#SMSociety),  an annual interdisciplinary academic conference organized by the Social Media Lab at Ryerson University.  The Conference features both quantitative and qualitative interdisciplinary works related to the broad theme of ‘Social Media & Society’. The first set of papers, published under this theme and being announced here, came from the 2014 Conference (Toronto, Canada, September 27-28).

This special theme is unique in a number of ways. First, it includes works that specifically focus on the intersection of big data and social media research. Second, papers in this theme take a user-centric perspective to studying big data practices. They do this by examining how or why different user groups rely on social media and in turn contribute to the rapid growth of user-generated big data. Finally, this special theme presents studies that take a more granular look at ‘big’ data through careful sampling and the application of both quantitative and qualitative methods. 

You can access the full text of papers in this special theme of Big Data & society at:

Friday, 22 January 2016

Editor Evelyn Ruppert responding to Frank Pasquale keynote on ‘The Promise (and Threat) of Algorithmic Accountability’

On Tuesday 26 January, 2016, Frank Pasquale, Professor of Law at the University of Maryland and author of The Black Box Society, will be delivering a public lecture at the launch of LSE’s MSc in Media and Communications (Data and Society). He will be focusing on recent controversies over the “right to be forgotten” and alternative credit scoring (such as proposals to base loan approvals on qualities of the applicant’s social network contacts), and propose reforms essential to humane automation of new media and banking. Editor Evelyn Ruppert will be responding to his lecture.

See for more details.

Tuesday, 19 January 2016

Deadline approaching: ISRF Essay Competition

The Independent Social Research Foundation (ISRF) and Big Data & Society (BD&S) announce the 2016 ISRF Early Career Researcher Essay Competition. A prize of CHF 1,000 will be awarded for the best 5,000 to 7,000 word essay on the topic 'Influence and Power'. Authors are encouraged to choose an essay title within this field. The winner will be invited to present their work at a special event at the Social Media & Society 2016 conference (Goldsmiths, University of London) and will have the conference fee waived and travel costs covered. Participants should either be current doctoral students or within three years of being awarded their doctorate. For more information including criteria and goals visit

We kindly remind you that the deadline for submissions is 31 January 2016.

Monday, 7 December 2015

Revisioning the Social and Human Sciences with Big Data: A Colloquium

By John W. Mohr, Ronald L. Breiger and Robin Wagner-Pacifici

The Special Theme that we have edited for Big Data & Society is entitled “Conceiving the Social with Big Data: A Colloquium of Social and Cultural Scientists.” It brings together 18 short essays by a number of scholars who are “early adopters” of new methods of analyzing Big Data to address issues in the social and human sciences.  The question we asked our contributors was how the use of these methods and these types of data can lead to different (implicit or explicit) understandings about how to think about the social.  As these essays make clear, there is also the important question of how working with Big Data can lead to changes at a deep level in researchers’ conceptions of the nature of science.

The resulting collection contains remarkable and incisive essays that raise a wide range of issues about how Big Data is increasingly implicated in practices and theorization in the humanities and social sciences.  As a way to synthesize this material we wrote a substantive introductory essay, “Ontologies, Methodologies and the Uses of Big Data in the Social and Cultural Sciences,” that summarizes our perspective on the essays and on how they raise a number of “puzzles about the locus and nature of human life, the nature of interpretation, the categorical constructions of individual entities and agents, the nature and relevance of contexts and temporalities, and the determinations of causality.”  We organize this discussion around a series of analytic binaries: Life/Data, Mind/Machine, and Induction/Deduction.

But the back story to this special theme is also interesting.  It begins with the Theory Section of the American Sociological Association (ASA).  Robin Wagner-Pacifici was the chair of this section in 2014-2015 and responsible for coming up with the conference program for the meetings in San Francisco.  She asked John Mohr to organize a session on the topic “Theory in the Era of Big Data.”  Mohr invited four papers and he asked Ronald Breiger to serve as the discussant for the panel.  The session itself was charged with energy and presented to a standing room only crowd.  In the discussion period, Gene Johnsen, a mathematician who works on network theory, stood up to request that we find a way to publish the papers (as a group) so that people could gain access to the materials and ideas that had been presented that day.

While Gene’s request was the original impetus for this collection we credit Kevin Lewis with the innovation that brought the special issue to fruition.  He suggested that we design this as a forum for short essays in which authors could reflect upon their own experiences in a more theoretical and reflexive manner thereby enabling a type of writing that they might not be able to express in a more conventional research article.  We quickly saw the wisdom in this vision and agreed.  We were able to include three of the original panelists who presented at the ASA Theory Session and they are represented here by the essay on “Wikipedia, Sociology, and the Promise and Pitfalls of Big Data” by Julia Adams and Hannah Brueckner, “The Paradox of Active Users,” by Patrick Park  and Michael Macy and “Big Data and the Danger of Being Precisely Inaccurate” by Daniel McFarland.  Ronald Breiger’s remarks as discussant formed the basis for the essay that he publishes here entitled “Scaling Down.”

From there we began to add to our list of sociologists who have been working in significant new ways with Big Data.  We invited Chris Bail (“Lost in a Random Forest: Using Big Data to Study Rare Events”), Paul DiMaggio (“Adapting Computational Text Analysis to Social Science (and Vice Versa”),  Amir Goldberg (“In Defense of Forensic Social Science”), Tim Hannigan (“Close Encounters of the Conceptual Kind:  Disambiguating Social Structure from Text”), Monica Lee and John Martin (“Surfeit and Surface”), Sophie Mützel (“Facing Big Data: Making Sociology Relevant ”) and Wouter de Nooy (“Structure from Interaction Events ”) to join the party. Peter Bearman (“Big Data and Historical Social Science”) was a late addition. Also, with more room to maneuver, we invited key contributors beyond sociology, including two innovative scholars from the humanities, Rachel Buurma (“Topic Modeling Against Totality: Anthony Trollope’s Barsetshire Series”) and Ted Underwood (“The Literary Uses of High-Dimensional Space”), and two pioneers from the information sciences, Jana Diesner (“Small Decisions with Big Impact for Data Analytics”) and Ryan Shaw (“Big Data and Reality”) to consider and reflect upon these same matters.

We approached Editor Evelyn Ruppert about publishing the collection in what was (at the time) this still rather new journal. She and her board were very supportive and the rest, as they say, is history. Or rather, the rest is this quite extraordinary collection of essays on the impacts of Big Data on the social and human sciences that we invite you to explore.