Publisher: Sage Publications   (Total: 1166 journals)

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Showing 1 - 200 of 1166 Journals sorted alphabetically
AADE in Practice     Hybrid Journal   (Followers: 6)
Abstracts in Anthropology     Full-text available via subscription   (Followers: 29)
Academic Pathology     Open Access   (Followers: 6)
Accounting History     Hybrid Journal   (Followers: 18, SJR: 0.527, CiteScore: 1)
Acta Radiologica     Hybrid Journal   (Followers: 1, SJR: 0.754, CiteScore: 2)
Acta Radiologica Open     Open Access   (Followers: 2)
Acta Sociologica     Hybrid Journal   (Followers: 39, SJR: 0.939, CiteScore: 2)
Action Research     Hybrid Journal   (Followers: 53, SJR: 0.308, CiteScore: 1)
Active Learning in Higher Education     Hybrid Journal   (Followers: 398, SJR: 1.397, CiteScore: 2)
Adaptive Behavior     Hybrid Journal   (Followers: 9, SJR: 0.288, CiteScore: 1)
Administration & Society     Hybrid Journal   (Followers: 18, SJR: 0.675, CiteScore: 1)
Adoption & Fostering     Hybrid Journal   (Followers: 25, SJR: 0.313, CiteScore: 0)
Adsorption Science & Technology     Open Access   (Followers: 9, SJR: 0.258, CiteScore: 1)
Adult Education Quarterly     Hybrid Journal   (Followers: 262, SJR: 0.566, CiteScore: 2)
Adult Learning     Hybrid Journal   (Followers: 51)
Advances in Dental Research     Hybrid Journal   (Followers: 11, SJR: 1.791, CiteScore: 4)
Advances in Developing Human Resources     Hybrid Journal   (Followers: 35, SJR: 0.614, CiteScore: 2)
Advances in Mechanical Engineering     Open Access   (Followers: 156, SJR: 0.272, CiteScore: 1)
Advances in Methods and Practices in Psychological Science     Full-text available via subscription   (Followers: 20)
Advances in Structural Engineering     Full-text available via subscription   (Followers: 51, SJR: 0.599, CiteScore: 1)
AERA Open     Open Access   (Followers: 14)
Affilia     Hybrid Journal   (Followers: 6, SJR: 0.496, CiteScore: 1)
Africa Spectrum     Open Access   (Followers: 17)
Agrarian South : J. of Political Economy     Hybrid Journal   (Followers: 3)
Air, Soil & Water Research     Open Access   (Followers: 13, SJR: 0.214, CiteScore: 1)
Alexandria : The J. of National and Intl. Library and Information Issues     Full-text available via subscription   (Followers: 68)
Allergy & Rhinology     Open Access   (Followers: 5)
AlterNative : An Intl. J. of Indigenous Peoples     Full-text available via subscription   (Followers: 39, SJR: 0.194, CiteScore: 0)
Alternative Law J.     Hybrid Journal   (Followers: 12, SJR: 0.176, CiteScore: 0)
Alternatives : Global, Local, Political     Hybrid Journal   (Followers: 12, SJR: 0.351, CiteScore: 1)
Alternatives to Laboratory Animals     Full-text available via subscription   (Followers: 11, SJR: 0.297, CiteScore: 1)
American Behavioral Scientist     Hybrid Journal   (Followers: 26, SJR: 0.982, CiteScore: 2)
American Economist     Hybrid Journal   (Followers: 7)
American Educational Research J.     Hybrid Journal   (Followers: 260, SJR: 2.913, CiteScore: 3)
American J. of Alzheimer's Disease and Other Dementias     Hybrid Journal   (Followers: 23, SJR: 0.67, CiteScore: 2)
American J. of Cosmetic Surgery     Hybrid Journal   (Followers: 9)
American J. of Evaluation     Hybrid Journal   (Followers: 18, SJR: 0.646, CiteScore: 2)
American J. of Health Promotion     Hybrid Journal   (Followers: 35, SJR: 0.807, CiteScore: 1)
American J. of Hospice and Palliative Medicine     Hybrid Journal   (Followers: 47, SJR: 0.65, CiteScore: 1)
American J. of Law & Medicine     Full-text available via subscription   (Followers: 12, SJR: 0.204, CiteScore: 1)
American J. of Lifestyle Medicine     Hybrid Journal   (Followers: 7, SJR: 0.431, CiteScore: 1)
American J. of Medical Quality     Hybrid Journal   (Followers: 13, SJR: 0.777, CiteScore: 1)
American J. of Men's Health     Open Access   (Followers: 9, SJR: 0.595, CiteScore: 2)
American J. of Rhinology and Allergy     Hybrid Journal   (Followers: 11, SJR: 0.972, CiteScore: 2)
American J. of Sports Medicine     Hybrid Journal   (Followers: 249, SJR: 3.949, CiteScore: 6)
American Politics Research     Hybrid Journal   (Followers: 36, SJR: 1.313, CiteScore: 1)
American Review of Public Administration     Hybrid Journal   (Followers: 28, SJR: 2.062, CiteScore: 2)
American Sociological Review     Hybrid Journal   (Followers: 358, SJR: 6.333, CiteScore: 6)
American String Teacher     Full-text available via subscription   (Followers: 3)
Analytical Chemistry Insights     Open Access   (Followers: 26, SJR: 0.224, CiteScore: 1)
Angiology     Hybrid Journal   (Followers: 5, SJR: 0.849, CiteScore: 2)
Animation     Hybrid Journal   (Followers: 15, SJR: 0.197, CiteScore: 0)
Annals of Clinical Biochemistry     Hybrid Journal   (Followers: 10, SJR: 0.634, CiteScore: 1)
Annals of Otology, Rhinology & Laryngology     Hybrid Journal   (Followers: 20, SJR: 0.807, CiteScore: 1)
Annals of Pharmacotherapy     Hybrid Journal   (Followers: 59, SJR: 1.096, CiteScore: 2)
Annals of the American Academy of Political and Social Science     Hybrid Journal   (Followers: 51, SJR: 1.225, CiteScore: 3)
Annals of the ICRP     Hybrid Journal   (Followers: 4, SJR: 0.548, CiteScore: 1)
Anthropocene Review     Hybrid Journal   (Followers: 8, SJR: 3.341, CiteScore: 7)
Anthropological Theory     Hybrid Journal   (Followers: 48, SJR: 0.739, CiteScore: 1)
Antitrust Bulletin     Hybrid Journal   (Followers: 14)
Antiviral Chemistry and Chemotherapy     Open Access   (Followers: 2, SJR: 0.635, CiteScore: 2)
Antyajaa : Indian J. of Women and Social Change     Hybrid Journal   (Followers: 1)
Applied Biosafety     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Applied Psychological Measurement     Hybrid Journal   (Followers: 21, SJR: 1.17, CiteScore: 1)
Applied Spectroscopy     Full-text available via subscription   (Followers: 27, SJR: 0.489, CiteScore: 2)
Armed Forces & Society     Hybrid Journal   (Followers: 25, SJR: 0.29, CiteScore: 1)
Arthaniti : J. of Economic Theory and Practice     Full-text available via subscription  
Arts and Humanities in Higher Education     Hybrid Journal   (Followers: 49, SJR: 0.305, CiteScore: 1)
Asia Pacific Media Educator     Hybrid Journal   (Followers: 1, SJR: 0.23, CiteScore: 0)
Asia-Pacific J. of Management Research and Innovation     Full-text available via subscription   (Followers: 3)
Asia-Pacific J. of Public Health     Hybrid Journal   (Followers: 15, SJR: 0.558, CiteScore: 1)
Asia-Pacific J. of Rural Development     Hybrid Journal   (Followers: 2)
Asian and Pacific Migration J.     Full-text available via subscription   (Followers: 8, SJR: 0.324, CiteScore: 1)
Asian Cardiovascular and Thoracic Annals     Hybrid Journal   (Followers: 2, SJR: 0.305, CiteScore: 0)
Asian J. of Comparative Politics     Hybrid Journal   (Followers: 5)
Asian J. of Legal Education     Full-text available via subscription   (Followers: 4)
Asian J. of Management Cases     Hybrid Journal   (Followers: 6, SJR: 0.101, CiteScore: 0)
ASN Neuro     Open Access   (Followers: 2, SJR: 1.534, CiteScore: 3)
Assessment     Hybrid Journal   (Followers: 19, SJR: 1.519, CiteScore: 3)
Assessment for Effective Intervention     Hybrid Journal   (Followers: 15, SJR: 0.578, CiteScore: 1)
Australasian J. of Early Childhood     Hybrid Journal   (Followers: 7, SJR: 0.535, CiteScore: 1)
Australasian Psychiatry     Hybrid Journal   (Followers: 18, SJR: 0.433, CiteScore: 1)
Australian & New Zealand J. of Psychiatry     Hybrid Journal   (Followers: 30, SJR: 1.801, CiteScore: 2)
Australian and New Zealand J. of Criminology     Hybrid Journal   (Followers: 547, SJR: 0.612, CiteScore: 1)
Australian J. of Career Development     Hybrid Journal   (Followers: 5)
Australian J. of Education     Hybrid Journal   (Followers: 51, SJR: 0.403, CiteScore: 1)
Australian J. of Management     Hybrid Journal   (Followers: 13, SJR: 0.497, CiteScore: 1)
Autism     Hybrid Journal   (Followers: 358, SJR: 1.739, CiteScore: 4)
Autism & Developmental Language Impairments     Open Access   (Followers: 17)
Avian Biology Research     Hybrid Journal   (Followers: 6, SJR: 0.401, CiteScore: 1)
Behavior Modification     Hybrid Journal   (Followers: 14, SJR: 0.877, CiteScore: 2)
Behavioral and Cognitive Neuroscience Reviews     Hybrid Journal   (Followers: 27)
Behavioral Disorders     Hybrid Journal   (Followers: 2)
Beyond Behavior     Hybrid Journal   (Followers: 2)
Bible Translator     Hybrid Journal   (Followers: 13)
Biblical Theology Bulletin     Hybrid Journal   (Followers: 24, SJR: 0.184, CiteScore: 0)
Big Data & Society     Open Access   (Followers: 55)
Biochemistry Insights     Open Access   (Followers: 7)
Bioinformatics and Biology Insights     Open Access   (Followers: 12, SJR: 1.141, CiteScore: 2)
Biological Research for Nursing     Hybrid Journal   (Followers: 7, SJR: 0.685, CiteScore: 2)
Biomarker Insights     Open Access   (Followers: 1, SJR: 0.81, CiteScore: 2)
Biomarkers in Cancer     Open Access   (Followers: 11)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Informatics Insights     Open Access   (Followers: 8)
Bioscope: South Asian Screen Studies     Hybrid Journal   (Followers: 4, SJR: 0.235, CiteScore: 0)
BMS: Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique     Hybrid Journal   (Followers: 4, SJR: 0.226, CiteScore: 0)
Body & Society     Hybrid Journal   (Followers: 29, SJR: 1.531, CiteScore: 3)
Bone and Tissue Regeneration Insights     Open Access   (Followers: 2)
Brain and Neuroscience Advances     Open Access  
Brain Science Advances     Open Access  
Breast Cancer : Basic and Clinical Research     Open Access   (Followers: 12, SJR: 0.823, CiteScore: 2)
British J. of Music Therapy     Hybrid Journal   (Followers: 9)
British J. of Occupational Therapy     Hybrid Journal   (Followers: 253, SJR: 0.323, CiteScore: 1)
British J. of Pain     Hybrid Journal   (Followers: 31, SJR: 0.579, CiteScore: 2)
British J. of Politics and Intl. Relations     Hybrid Journal   (Followers: 39, SJR: 0.91, CiteScore: 2)
British J. of Visual Impairment     Hybrid Journal   (Followers: 14, SJR: 0.337, CiteScore: 1)
British J.ism Review     Hybrid Journal   (Followers: 18)
BRQ Business Review Quarterly     Open Access   (Followers: 1)
Building Acoustics     Hybrid Journal   (Followers: 4, SJR: 0.215, CiteScore: 1)
Building Services Engineering Research & Technology     Hybrid Journal   (Followers: 3, SJR: 0.583, CiteScore: 1)
Bulletin of Science, Technology & Society     Hybrid Journal   (Followers: 9)
Business & Society     Hybrid Journal   (Followers: 15)
Business and Professional Communication Quarterly     Hybrid Journal   (Followers: 9, SJR: 0.348, CiteScore: 1)
Business Information Review     Hybrid Journal   (Followers: 17, SJR: 0.279, CiteScore: 0)
Business Perspectives and Research     Hybrid Journal   (Followers: 3)
Cahiers Élisabéthains     Hybrid Journal   (Followers: 1, SJR: 0.111, CiteScore: 0)
Calcutta Statistical Association Bulletin     Hybrid Journal   (Followers: 1)
California Management Review     Hybrid Journal   (Followers: 37, SJR: 2.209, CiteScore: 4)
Canadian Association of Radiologists J.     Full-text available via subscription   (Followers: 2, SJR: 0.463, CiteScore: 1)
Canadian J. of Kidney Health and Disease     Open Access   (Followers: 8, SJR: 1.007, CiteScore: 2)
Canadian J. of Nursing Research (CJNR)     Hybrid Journal   (Followers: 15)
Canadian J. of Occupational Therapy     Hybrid Journal   (Followers: 168, SJR: 0.626, CiteScore: 1)
Canadian J. of Psychiatry     Hybrid Journal   (Followers: 28, SJR: 1.769, CiteScore: 3)
Canadian J. of School Psychology     Hybrid Journal   (Followers: 12, SJR: 0.266, CiteScore: 1)
Canadian Pharmacists J. / Revue des Pharmaciens du Canada     Hybrid Journal   (Followers: 3, SJR: 0.536, CiteScore: 1)
Cancer Control     Open Access   (Followers: 2)
Cancer Growth and Metastasis     Open Access   (Followers: 1)
Cancer Informatics     Open Access   (Followers: 4, SJR: 0.64, CiteScore: 1)
Capital and Class     Hybrid Journal   (Followers: 10, SJR: 0.282, CiteScore: 1)
Cardiac Cath Lab Director     Full-text available via subscription   (Followers: 1)
Cardiovascular and Thoracic Open     Open Access   (Followers: 1)
Career Development and Transition for Exceptional Individuals     Hybrid Journal   (Followers: 10, SJR: 0.44, CiteScore: 1)
Cartilage     Hybrid Journal   (Followers: 6, SJR: 0.889, CiteScore: 3)
Cell Transplantation     Open Access   (Followers: 5, SJR: 1.023, CiteScore: 3)
Cephalalgia     Hybrid Journal   (Followers: 8, SJR: 1.581, CiteScore: 3)
Cephalalgia Reports     Open Access   (Followers: 4)
Child Language Teaching and Therapy     Hybrid Journal   (Followers: 34, SJR: 0.501, CiteScore: 1)
Child Maltreatment     Hybrid Journal   (Followers: 11, SJR: 1.22, CiteScore: 3)
Child Neurology Open     Open Access   (Followers: 6)
Childhood     Hybrid Journal   (Followers: 19, SJR: 0.894, CiteScore: 2)
Childhood Obesity and Nutrition     Open Access   (Followers: 12)
China Information     Hybrid Journal   (Followers: 9, SJR: 0.767, CiteScore: 2)
China Report     Hybrid Journal   (Followers: 11, SJR: 0.221, CiteScore: 0)
Chinese J. of Sociology     Full-text available via subscription   (Followers: 5)
Christian Education J. : Research on Educational Ministry     Hybrid Journal   (Followers: 1)
Chronic Illness     Hybrid Journal   (Followers: 6, SJR: 0.672, CiteScore: 2)
Chronic Respiratory Disease     Hybrid Journal   (Followers: 12, SJR: 0.808, CiteScore: 2)
Chronic Stress     Open Access  
Citizenship, Social and Economics Education     Full-text available via subscription   (Followers: 6, SJR: 0.145, CiteScore: 0)
Cleft Palate-Craniofacial J.     Hybrid Journal   (Followers: 8, SJR: 0.757, CiteScore: 1)
Clin-Alert     Hybrid Journal   (Followers: 1)
Clinical and Applied Thrombosis/Hemostasis     Open Access   (Followers: 32, SJR: 0.49, CiteScore: 1)
Clinical and Translational Neuroscience     Open Access   (Followers: 1)
Clinical Case Studies     Hybrid Journal   (Followers: 3, SJR: 0.364, CiteScore: 1)
Clinical Child Psychology and Psychiatry     Hybrid Journal   (Followers: 45, SJR: 0.73, CiteScore: 2)
Clinical EEG and Neuroscience     Hybrid Journal   (Followers: 8, SJR: 0.552, CiteScore: 2)
Clinical Ethics     Hybrid Journal   (Followers: 13, SJR: 0.296, CiteScore: 1)
Clinical Medicine Insights : Arthritis and Musculoskeletal Disorders     Open Access   (Followers: 3, SJR: 0.537, CiteScore: 2)
Clinical Medicine Insights : Blood Disorders     Open Access   (Followers: 1, SJR: 0.314, CiteScore: 2)
Clinical Medicine Insights : Cardiology     Open Access   (Followers: 8, SJR: 0.686, CiteScore: 2)
Clinical Medicine Insights : Case Reports     Open Access   (Followers: 1, SJR: 0.283, CiteScore: 1)
Clinical Medicine Insights : Circulatory, Respiratory and Pulmonary Medicine     Open Access   (Followers: 4, SJR: 0.425, CiteScore: 2)
Clinical Medicine Insights : Ear, Nose and Throat     Open Access   (Followers: 2)
Clinical Medicine Insights : Endocrinology and Diabetes     Open Access   (Followers: 34, SJR: 0.63, CiteScore: 2)
Clinical Medicine Insights : Oncology     Open Access   (Followers: 3, SJR: 1.129, CiteScore: 3)
Clinical Medicine Insights : Pediatrics     Open Access   (Followers: 3)
Clinical Medicine Insights : Psychiatry     Open Access   (Followers: 10)
Clinical Medicine Insights : Reproductive Health     Open Access   (Followers: 1, SJR: 0.776, CiteScore: 0)
Clinical Medicine Insights : Therapeutics     Open Access   (Followers: 1, SJR: 0.172, CiteScore: 0)
Clinical Medicine Insights : Trauma and Intensive Medicine     Open Access   (Followers: 4)
Clinical Medicine Insights : Urology     Open Access   (Followers: 3)
Clinical Medicine Insights : Women's Health     Open Access   (Followers: 4)
Clinical Nursing Research     Hybrid Journal   (Followers: 34, SJR: 0.471, CiteScore: 1)
Clinical Pathology     Open Access   (Followers: 5)
Clinical Pediatrics     Hybrid Journal   (Followers: 25, SJR: 0.487, CiteScore: 1)
Clinical Psychological Science     Hybrid Journal   (Followers: 16, SJR: 3.281, CiteScore: 5)
Clinical Rehabilitation     Hybrid Journal   (Followers: 78, SJR: 1.322, CiteScore: 3)
Clinical Risk     Hybrid Journal   (Followers: 5, SJR: 0.133, CiteScore: 0)
Clinical Trials     Hybrid Journal   (Followers: 22, SJR: 2.399, CiteScore: 2)
Clothing and Textiles Research J.     Hybrid Journal   (Followers: 28, SJR: 0.36, CiteScore: 1)
Collections : A J. for Museum and Archives Professionals     Full-text available via subscription   (Followers: 3)
Common Law World Review     Full-text available via subscription   (Followers: 17)
Communication & Sport     Hybrid Journal   (Followers: 8, SJR: 0.385, CiteScore: 1)
Communication and the Public     Hybrid Journal   (Followers: 2)
Communication Disorders Quarterly     Hybrid Journal   (Followers: 15, SJR: 0.458, CiteScore: 1)
Communication Research     Hybrid Journal   (Followers: 24, SJR: 2.171, CiteScore: 3)
Community College Review     Hybrid Journal   (Followers: 8, SJR: 1.451, CiteScore: 1)
Comparative Political Studies     Hybrid Journal   (Followers: 293, SJR: 3.772, CiteScore: 3)
Compensation & Benefits Review     Hybrid Journal   (Followers: 8)
Competition & Change     Hybrid Journal   (Followers: 12, SJR: 0.843, CiteScore: 2)

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Similar Journals
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Big Data & Society
Number of Followers: 55  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2053-9517
Published by Sage Publications Homepage  [1166 journals]
  • Big data and Belmont: On the ethics and research implications of
           consumer-based datasets

    • Authors: Remy Stewart
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Consumer-based datasets are the products of data brokerage firms that agglomerate millions of personal records on the adult US population. This big data commodity is purchased by both companies and individual clients for purposes such as marketing, risk prevention, and identity searches. The sheer magnitude and population coverage of available consumer-based datasets and the opacity of the business practices that create these datasets pose emergent ethical challenges within the computational social sciences that have begun to incorporate consumer-based datasets into empirical research. To directly engage with the core ethical debates around the use of consumer-based datasets within social science research, I first consider two case study applications of consumer-based dataset-based scholarship. I then focus on three primary ethical dilemmas within consumer-based datasets regarding human subject research, participant privacy, and informed consent in conversation with the principles of the seminal Belmont Report.
      Citation: Big Data & Society
      PubDate: 2021-10-16T12:12:43Z
      DOI: 10.1177/20539517211048183
      Issue No: Vol. 8, No. 2 (2021)
  • Squeaky wheels: Missing data, disability, and power in the smart city

    • Authors: Shiloh Deitz, Amy Lobben, Arielle Alferez
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Data about the accessibility of United States municipalities is infrastructure in the smart city. What is counted and how, reflects the sociotechnical imaginary (norms and values) of a time or place. In this paper we focus on features identified by people with disabilities as promoting or hindering safe pedestrian travel. We use a regionally stratified sample of 178 cities across the United States. The municipalities were scored on two factors: their open data practices (or lack thereof), and the degree to which they cataloged the environmental features that persons with disabilities deemed critical for safe movement through urban spaces. In contradiction to the dominating narrative of too much data and not enough analyses, we find that when it comes to data points that might be useful to persons with disabilities, data are lacking. This data gap has consequences both politically and materially—on one hand data could help enforce compliance with the Americans with Disabilities Act, on the other they would allow for safe route planning. We find that reading these data formats and collection patterns from the perspective of critical disability studies—particularly those whose work disrupts notions of “normal” —helps answer questions about potential benefits and harms of data practices. This lens has the potential to promote analysis that is as disruptive to injustices as it is practical.
      Citation: Big Data & Society
      PubDate: 2021-10-16T12:12:23Z
      DOI: 10.1177/20539517211047735
      Issue No: Vol. 8, No. 2 (2021)
  • The fabrics of machine moderation: Studying the technical, normative, and
           organizational structure of Perspective API

    • Authors: Bernhard Rieder, Yarden Skop
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Over recent years, the stakes and complexity of online content moderation have been steadily raised, swelling from concerns about personal conflict in smaller communities to worries about effects on public life and democracy. Because of the massive growth in online expressions, automated tools based on machine learning are increasingly used to moderate speech. While ‘design-based governance’ through complex algorithmic techniques has come under intense scrutiny, critical research covering algorithmic content moderation is still rare. To add to our understanding of concrete instances of machine moderation, this article examines Perspective API, a system for the automated detection of ‘toxicity’ developed and run by the Google unit Jigsaw that can be used by websites to help moderate their forums and comment sections. The article proceeds in four steps. First, we present our methodological strategy and the empirical materials we were able to draw on, including interviews, documentation, and GitHub repositories. We then summarize our findings along five axes to identify the various threads Perspective API brings together to deliver a working product. The third section discusses two conflicting organizational logics within the project, paying attention to both critique and what can be learned from the specific case at hand. We conclude by arguing that the opposition between ‘human’ and ‘machine’ in speech moderation obscures the many ways these two come together in concrete systems, and suggest that the way forward requires proactive engagement with the design of technologies as well as the institutions they are embedded in.
      Citation: Big Data & Society
      PubDate: 2021-10-16T12:12:08Z
      DOI: 10.1177/20539517211046181
      Issue No: Vol. 8, No. 2 (2021)
  • Ghosts of white methods' The challenges of Big Data research in
           exploring racism in digital context

    • Authors: Kaarina Nikunen
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      The paper explores the potential and limitations of big data for researching racism on social media. Informed by critical data studies and critical race studies, the paper discusses challenges of doing big data research and the problems of the so called ‘white method’. The paper introduces the following three types of approach, each with a different epistemological basis for researching racism in digital context: 1) using big data analytics to point out the dominant power relations and the dynamics of racist discourse, 2) complementing big data with qualitative research and 3) revealing new logics of racism in datafied context. The paper contributes to critical data and critical race studies by enhancing the understanding of the possibilities and limitations of big data research. This study also highlights the importance of contextualisation and mixed methods for achieving a more nuanced comprehension of racism and discrimination on social media and in large datasets.
      Citation: Big Data & Society
      PubDate: 2021-10-07T11:55:02Z
      DOI: 10.1177/20539517211048964
      Issue No: Vol. 8, No. 2 (2021)
  • “AI will fix this” – The Technical, Discursive, and Political Turn
           to AI in Governing Communication

    • Authors: Christian Katzenbach
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Technologies of “artificial intelligence” (AI) and machine learning (ML) are increasingly presented as solutions to key problems of our societies. Companies are developing, investing in, and deploying machine learning applications at scale in order to filter and organize content, mediate transactions, and make sense of massive sets of data. At the same time, social and legal expectations are ambiguous, and the technical challenges are substantial.This is the introductory article to a special theme that addresses this turn to AI as a technical, discursive and political phenomena. The opening article contextualizes this theme by unfolding this multi-layered nature of the turn to AI. It argues that, whereas public and economic discourses position the widespread deployment of AI and automation in the governance of digital communication as a technical turn with a narrative of revolutionary breakthrough-moments and of technological progress, this development is at least similarly dependent on a parallel discursive and political turn to AI. The article positions the current turn to AI in the longstanding motif of the “technological fix” in the relationship between technology and society, and identifies a discursive turn to responsibility in platform governance as a key driver for AI and automation. In addition, a political turn to more demanding liability rules for platforms further incentivizes platforms to automatically screen their content for possibly infringing or violating content, and position AI as a solution to complex social problems.
      Citation: Big Data & Society
      PubDate: 2021-10-04T10:52:31Z
      DOI: 10.1177/20539517211046182
      Issue No: Vol. 8, No. 2 (2021)
  • For what it's worth. Unearthing the values embedded in digital phenotyping
           for mental health

    • Authors: Rasmus Birk, Anna Lavis, Federica Lucivero, Gabrielle Samuel
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Digital phenotyping for mental health is an emerging trend which uses digital data, derived from mobile applications, wearable technologies and digital sensors, to measure, track and predict the mental health of an individual. Digital phenotyping for mental health is a growing, but as yet underexamined, field. As we will show, the rapid growth of digital phenotyping for mental health raises crucial questions about the values that underpin and are reinforced by this technology, as well as regarding to whom it may become valuable. In this commentary, we explore these questions by focusing on the construction of value across two interrelated domains: user experience and epistemologies on the one hand, and issues of data and ownership on the other. In doing so, we demonstrate the need for a deeper ethical and epistemological engagement with the value assumptions that underpin the promise of digital phenotyping for mental health.
      Citation: Big Data & Society
      PubDate: 2021-10-04T10:51:31Z
      DOI: 10.1177/20539517211047319
      Issue No: Vol. 8, No. 2 (2021)
  • For a heterodox computational social science

    • Authors: Petter Törnberg, Justus Uitermark
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that neoclassical economics does for neoliberalism. In recognition of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life.
      Citation: Big Data & Society
      PubDate: 2021-10-04T10:50:53Z
      DOI: 10.1177/20539517211047725
      Issue No: Vol. 8, No. 2 (2021)
  • Racial formations as data formations

    • Authors: Thao Phan, Scott Wark
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      This commentary uses Paul Gilroy’s controversial claim that new technoscientific processes are instituting an ‘end to race’ as a provocation to discuss the epistemological transformation of race in algorithmic culture. We situate Gilroy’s provocation within the context of an abolitionist agenda against racial-thinking, underscoring the relationship between his post-race polemic and a post-visual discourse. We then discuss the challenges of studying race within regimes of computation, which rely on structures that are, for the most part, opaque; in particular, modes of classification that operate through proxies and abstractions and that figure racialized bodies not as single, coherent subjects, but as shifting clusters of data. We argue that in this new regime, race emerges as an epiphenomenon of processes of classifying and sorting – what we call ‘racial formations as data formations’. This discussion is significant because it raises new theoretical, methodological and political questions for scholars of media and critical algorithmic studies. It asks: how are we supposed to think, to identify and to confront race and racialisation when they vanish into algorithmic systems that are beyond our perception' What becomes of racial formations in post-visual regimes'
      Citation: Big Data & Society
      PubDate: 2021-09-30T10:44:50Z
      DOI: 10.1177/20539517211046377
      Issue No: Vol. 8, No. 2 (2021)
  • The great transformer: Examining the role of large language models in the
           political economy of AI

    • Authors: Dieuwertje Luitse, Wiebke Denkena
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      In recent years, AI research has become more and more computationally demanding. In natural language processing (NLP), this tendency is reflected in the emergence of large language models (LLMs) like GPT-3. These powerful neural network-based models can be used for a range of NLP tasks and their language generation capacities have become so sophisticated that it can be very difficult to distinguish their outputs from human language. LLMs have raised concerns over their demonstrable biases, heavy environmental footprints, and future social ramifications. In December 2020, critical research on LLMs led Google to fire Timnit Gebru, co-lead of the company’s AI Ethics team, which sparked a major public controversy around LLMs and the growing corporate influence over AI research. This article explores the role LLMs play in the political economy of AI as infrastructural components for AI research and development. Retracing the technical developments that have led to the emergence of LLMs, we point out how they are intertwined with the business model of big tech companies and further shift power relations in their favour. This becomes visible through the Transformer, which is the underlying architecture of most LLMs today and started the race for ever bigger models when it was introduced by Google in 2017. Using the example of GPT-3, we shed light on recent corporate efforts to commodify LLMs through paid API access and exclusive licensing, raising questions around monopolization and dependency in a field that is increasingly divided by access to large-scale computing power.
      Citation: Big Data & Society
      PubDate: 2021-09-29T01:59:06Z
      DOI: 10.1177/20539517211047734
      Issue No: Vol. 8, No. 2 (2021)
  • Big Data Dreams and Reality in Shenzhen: An Investigation of Smart City
           Implementation in China

    • Authors: Jelena Große-Bley, Genia Kostka
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Chinese cities are increasingly using digital technologies to address urban problems and govern society. However, little is known about how this digital transition has been implemented. This study explores the introduction of digital governance in Shenzhen, one of China's most advanced smart cities. We show that, at the local level, the successful implementation of digital systems faces numerous hurdles in long-standing data management and bureaucratic practices that are at least as challenging as the technical problems. Furthermore, the study finds that the digital systems in Shenzhen entail a creeping centralisation of data that potentially turns lower administrative government units into mere users of the city-level smart platforms rather than being in control of their own data resources. Smart city development and big data ambitions thereby imply shifting stakeholder relations at the local level and also pull non-governmental stakeholders, such as information technology companies and research institutions, closer to new data flows and smart governance systems. The findings add to the discussion of big data-driven smart systems and their implications for governance processes in an authoritarian context.
      Citation: Big Data & Society
      PubDate: 2021-09-29T01:58:34Z
      DOI: 10.1177/20539517211045171
      Issue No: Vol. 8, No. 2 (2021)
  • Controversing the datafied smart city: Conceptualising a
           ‘making-controversial’ approach to civic engagement

    • Authors: Corelia Baibarac-Duignan, Michiel de Lange
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      In this paper, we propose the concept of controversing as an approach for engaging citizens in debates around the datafied city and in shaping responsible smart cities that incorporate diverse public values. Controversing addresses the engagement of citizens in discussions about the datafication of urban life by productively deploying controversies around data. Attempts to engage citizens in the smart city frequently involve ‘neutral’ data visualisations aimed at making abstract sociotechnical issues more tangible. In addition, citizens are meant to gather around issues already defined externally by others. Instead, we focus on how people might become engaged and develop the capacity to shape alternative urban futures. We suggest that making controversial apparently less contentious issues in the smart city allows people to identify their own issues, come together temporarily as a public, imagine alternative possibilities and thus develop capacities for action. In this context, controversies can act as agents of change and open up new spaces for participation and action. We develop the notion of controversing as a deliberate strategy of making datafication controversial, and operationalise the term along the dimensions of recontextualisation, meaning-making and agency. We then look at two cases from the mid-sized city of Amersfoort in the Netherlands, first to test the conceptual potential of controversing to expose how frictions shape citizen engagement, and second to analyse how controversing may frame design-oriented methods aimed at involving diverse participants in discussing datafication and defining public values in the datafied smart city.
      Citation: Big Data & Society
      PubDate: 2021-09-28T02:16:54Z
      DOI: 10.1177/20539517211025557
      Issue No: Vol. 8, No. 2 (2021)
  • On the genealogy of machine learning datasets: A critical history of

    • Authors: Emily Denton, Alex Hanna, Razvan Amironesei, Andrew Smart, Hilary Nicole
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny. Many of these examinations have focused on the contents of machine learning datasets, finding glaring underrepresentation of minoritized groups. In contrast, relatively little work has been done to examine the norms, values, and assumptions embedded in these datasets. In this work, we conceptualize machine learning datasets as a type of informational infrastructure, and motivate a genealogy as method in examining the histories and modes of constitution at play in their creation. We present a critical history of ImageNet as an exemplar, utilizing critical discourse analysis of major texts around ImageNet’s creation and impact. We find that assumptions around ImageNet and other large computer vision datasets more generally rely on three themes: the aggregation and accumulation of more data, the computational construction of meaning, and making certain types of data labor invisible. By tracing the discourses that surround this influential benchmark, we contribute to the ongoing development of the standards and norms around data development in machine learning and artificial intelligence research.
      Citation: Big Data & Society
      PubDate: 2021-09-24T02:19:06Z
      DOI: 10.1177/20539517211035955
      Issue No: Vol. 8, No. 2 (2021)
  • Different types of COVID-19 misinformation have different emotional
           valence on Twitter

    • Authors: Marina Charquero-Ballester, Jessica G Walter, Ida A Nissen, Anja Bechmann
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      The spreading of COVID-19 misinformation on social media could have severe consequences on people's behavior. In this paper, we investigated the emotional expression of misinformation related to the COVID-19 crisis on Twitter and whether emotional valence differed depending on the type of misinformation. We collected 17,463,220 English tweets with 76 COVID-19-related hashtags for March 2020. Using Google Fact Check Explorer API we identified 226 unique COVID-19 false stories for March 2020. These were clustered into six types of misinformation (cures, virus, vaccine, politics, conspiracy theories, and other). Applying the 226 classifiers to the Twitter sample we identified 690,004 tweets. Instead of running the sentiment on all tweets we manually coded a random subset of 100 tweets for each classifier to increase the validity, reducing the dataset to 2,097 tweets. We found that only a minor part of the entire dataset was related to misinformation. Also, misinformation in general does not lean towards a certain emotional valence. However, looking at comparisons of emotional valence for different types of misinformation uncovered that misinformation related to “virus” and “conspiracy” had a more negative valence than “cures,” “vaccine,” “politics,” and “other.” Knowing from existing studies that negative misinformation spreads faster, this demonstrates that filtering for misinformation type is fruitful and indicates that a focus on “virus” and “conspiracy” could be one strategy in combating misinformation. As emotional contexts affect misinformation spreading, the knowledge about emotional valence for different types of misinformation will help to better understand the spreading and consequences of misinformation.
      Citation: Big Data & Society
      PubDate: 2021-09-22T01:01:02Z
      DOI: 10.1177/20539517211041279
      Issue No: Vol. 8, No. 2 (2021)
  • A view from anthropology: Should anthropologists fear the data

    • Authors: Kristoffer Albris, Eva I Otto, Sofie L Astrupgaard, Emilie Munch Gregersen, Laura Skousgaard Jørgensen, Olivia Jørgensen, Clara Rosa Sandbye, Signe Schønning
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      If you are an anthropologist wanting to use digital methods or programming as part of your research, where do you start' In this commentary, we discuss three ways in which anthropologists can use computational tools to enhance, support, and complement ethnographic methods. By presenting our reflections, we hope to contribute to the stirring conversations about the potential future role(s) of (social) data science vis-a-vis anthropology and ethnography, and to inspire other anthropologists to take up the use of digital methods, programming, and computational tools in their own research.
      Citation: Big Data & Society
      PubDate: 2021-09-15T05:04:55Z
      DOI: 10.1177/20539517211043655
      Issue No: Vol. 8, No. 2 (2021)
  • Online Labour Index 2020: New ways to measure the world’s remote
           freelancing market

    • Authors: Fabian Stephany, Otto Kässi, Uma Rani, Vili Lehdonvirta
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      The Online Labour Index (OLI) was launched in 2016 to measure the global utilisation of online freelance work at scale. Five years after its creation, the OLI has become a point of reference for scholars and policy experts investigating the online gig economy. As the market for online freelancing work matures, a high volume of data and new analytical tools allow us to revisit half a decade of online freelance monitoring and extend the index's scope to more dimensions of the global online freelancing market. While (still) measuring the utilisation of online labour across countries and occupations by tracking the number of projects and tasks posted on major English-language platforms, the new Online Labour Index 2020 (OLI 2020) also tracks Spanish- and Russian-language platforms, reveals changes over time in the geography of labour supply and estimates female participation in the online gig economy. The rising popularity of software and tech work and the concentration of freelancers on the Indian subcontinent are examples of the insights that the OLI 2020 provides. The OLI 2020 delivers a more detailed picture of the world of online freelancing via an interactive online visualisation updated daily. It provides easy access to downloadable open data for policymakers, labour market researchers, and the general public (
      Citation: Big Data & Society
      PubDate: 2021-09-15T05:03:35Z
      DOI: 10.1177/20539517211043240
      Issue No: Vol. 8, No. 2 (2021)
  • Discovering needs for digital capitalism: The hybrid profession of data

    • Authors: Robert Dorschel
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Over the last decade, ‘data scientists’ have burst into society as a novel expert role. They hold increasing responsibility for generating and analysing digitally captured human experiences. The article considers their professionalization not as a functionally necessary development but as the outcome of classification practices and struggles. The rise of data scientists is examined across their discursive classification in the academic and economic fields in both the USA and Germany. Despite notable differences across these fields and nations, the article identifies two common subjectivation patterns. Firstly, data scientists are constructed as hybrids, who combine generally conflictive roles as both generalists and specialists; technicians and communicators; data exploiters and data ethicists. This finding is interpreted as demonstrating a discursive distinction between data scientists and other competing and supposedly more one-dimensional professionals, such as statisticians or computer scientists. Secondly, the article uncovers a discursive construction that interpellates data scientists as discoverers of needs. They are imagined as explorative work subjects who can establish growth for digital capitalism by generating behavioural patterns that allow for personalization, customization and optimization practices.
      Citation: Big Data & Society
      PubDate: 2021-09-15T05:02:15Z
      DOI: 10.1177/20539517211040760
      Issue No: Vol. 8, No. 2 (2021)
  • Excavating awareness and power in data science: A manifesto for
           trustworthy pervasive data research

    • Authors: Katie Shilton, Emanuel Moss, Sarah A. Gilbert, Matthew J. Bietz, Casey Fiesler, Jacob Metcalf, Jessica Vitak, Michael Zimmer
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science.
      Citation: Big Data & Society
      PubDate: 2021-09-15T05:00:32Z
      DOI: 10.1177/20539517211040759
      Issue No: Vol. 8, No. 2 (2021)
  • A view from data science

    • Authors: Anna Sapienza, Sune Lehmann
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      For better and worse, our world has been transformed by Big Data. To understand digital traces generated by individuals, we need to design multidisciplinary approaches that combine social and data science. Data and social scientists face the challenge of effectively building upon each other’s approaches to overcome the limitations inherent in each side. Here, we offer a “data science perspective” on the challenges that arise when working to establish this interdisciplinary environment. We discuss how we perceive the differences and commonalities of the questions we ask to understand digital behaviors (including how we answer them), and how our methods may complement each other. Finally, we describe what a path toward common ground between these fields looks like when viewed from data science.
      Citation: Big Data & Society
      PubDate: 2021-09-15T04:58:52Z
      DOI: 10.1177/20539517211040198
      Issue No: Vol. 8, No. 2 (2021)
  • Four investment areas for ethical AI: Transdisciplinary opportunities to
           close the publication-to-practice gap

    • Authors: Jana Schaich Borg
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this Ethical Artificial Intelligence publication-to-practice gap, maximizing the positive impact Artificial Intelligence and Big Data have on society. For Ethical Artificial Intelligence to become a reality, I argue that these areas need to be addressed holistically in a way that acknowledges their interdependencies. Progress will require iteration, compromise, and transdisciplinary collaboration, but the result of our investments will be the realization of Artificial Intelligence's and Big Data's tremendous potential for social good, in practice rather than in just our hopes and aspirations.
      Citation: Big Data & Society
      PubDate: 2021-09-15T04:57:33Z
      DOI: 10.1177/20539517211040197
      Issue No: Vol. 8, No. 2 (2021)
  • Computational challenges to test and revitalize Claude Lévi-Strauss
           transformational methodology

    • Authors: Albert Doja, Laurent Capocchi, Jean-François Santucci
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization of selected narratives, aimed at validating and revitalizing the transformational and morphodynamic theory and methodology proposed by Claude Lévi-Strauss in his structural analysis of myth. After an analysis of Lévi-Strauss’s transformational methodology, we describe in detail how discrete-event system specification models are implemented and developed in the framework of a DEVSimPy software environment. The validation of the method involves a discrete-event system specification simulation based on the extension of discrete-event system specification models dedicated to provide a dynamic Google Earth visualization of the selected myth. Future work around the discrete-event system specification formalism in anthropology is described as well as future applications regarding the impact of computational models (discrete-event system specification formalism, Bayesian inferences, and object-oriented features) to new contemporary anthropological domains.
      Citation: Big Data & Society
      PubDate: 2021-09-08T04:19:07Z
      DOI: 10.1177/20539517211037862
      Issue No: Vol. 8, No. 2 (2021)
  • Digital phenotyping and data inheritance

    • Authors: Sara Green, Mette N. Svendsen
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Proponents of precision medicine envision that digital phenotyping can enable more individualized strategies to manage current and future health conditions. We problematize the interpretation of digital phenotypes as straightforward representations of individuals through examples of what we call data inheritance. Rather than being a digital copy of a presumed original, digital phenotypes are shaped by larger data collectives that precede and continuously change how the individual is represented. We contend that looking beyond the individual is crucial for understanding the factors that can ‘bend’ digital mirrors in specific directions. Since algorithms used for digital profiling are based on historical data, their predictions often inherit and increase the values and perspectives of past data practices. Moreover, the data legacies we leave behind today may return as so-called ‘data phantoms’ that conflict with the interests of the individual and contest who and what the ‘original’ is.
      Citation: Big Data & Society
      PubDate: 2021-09-06T10:41:53Z
      DOI: 10.1177/20539517211036799
      Issue No: Vol. 8, No. 2 (2021)
  • Towards a United Nations Internal Regulation for Artificial Intelligence

    • Authors: Eleonore Fournier-Tombs
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      This article sets out the rationale for a United Nations Regulation for Artificial Intelligence, which is needed to set out the modes of engagement of the organisation when using artificial intelligence technologies in the attainment of its mission. It argues that given the increasing use of artificial intelligence by the United Nations, including in some activities considered high risk by the European Commission, a regulation is urgent. It also contends that rules of engagement for artificial intelligence at the United Nations would support the development of ‘good artificial intelligence’, by giving developers clear pathways for authorisation that would build trust in these technologies. Finally, it argues that an internal regulation would build upon the work in artificial intelligence ethics and best practices already initiated in the organisation that could, like the Brussels Effect, set an important precedent for regulations in other countries.
      Citation: Big Data & Society
      PubDate: 2021-08-30T12:58:57Z
      DOI: 10.1177/20539517211039493
      Issue No: Vol. 8, No. 2 (2021)
  • Bot, or not' Comparing three methods for detecting social bots in five
           political discourses

    • Authors: Franziska Martini, Paul Samula, Tobias R Keller, Ulrike Klinger
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Social bots – partially or fully automated accounts on social media platforms – have not only been widely discussed, but have also entered political, media and research agendas. However, bot detection is not an exact science. Quantitative estimates of bot prevalence vary considerably and comparative research is rare. We show that findings on the prevalence and activity of bots on Twitter depend strongly on the methods used to identify automated accounts. We search for bots in political discourses on Twitter, using three different bot detection methods: Botometer, Tweetbotornot and “heavy automation”. We drew a sample of 122,884 unique user Twitter accounts that had produced 263,821 tweets contributing to five political discourses in five Western democracies. While all three bot detection methods classified accounts as bots in all our cases, the comparison shows that the three approaches produce very different results. We discuss why neither manual validation nor triangulation resolves the basic problems, and conclude that social scientists studying the influence of social bots on (political) communication and discourse dynamics should be careful with easy-to-use methods, and consider interdisciplinary research.
      Citation: Big Data & Society
      PubDate: 2021-08-24T06:09:17Z
      DOI: 10.1177/20539517211033566
      Issue No: Vol. 8, No. 2 (2021)
  • Data deprivations, data gaps and digital divides: Lessons from the
           COVID-19 pandemic

    • Authors: Wim Naudé, Ricardo Vinuesa
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges; (iv) lack of interoperability and difficulty reusing data will limit value from data; (v) data deprivation, digital gaps and digital divides are not just a by-product of unequal global development, but will magnify the unequal impacts of a global crisis, and will be magnified in turn by global crises; (vi) having more data and even better data analytical techniques, such as artificial intelligence, does not guarantee that development outcomes will improve; (vii) decentralised data gathering and use can help to build trust – particularly important for coordination of behaviour.
      Citation: Big Data & Society
      PubDate: 2021-08-20T05:09:10Z
      DOI: 10.1177/20539517211025545
      Issue No: Vol. 8, No. 2 (2021)
  • “The revolution will not be supervised”: Consent and open
           secrets in data science

    • Authors: Coleen Carrigan, Madison W Green, Abibat Rahman-Davies
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      The social impacts of computer technology are often glorified in public discourse, but there is growing concern about its actual effects on society. In this article, we ask: how does “consent” as an analytical framework make visible the social dynamics and power relations in the capture, extraction, and labor of data science knowledge production' We hypothesize that a form of boundary violation in data science workplaces—gender harassment—may correlate with the ways humans’ lived experiences are extracted to produce Big Data. The concept of consent offers a useful way to draw comparisons between gender relations in data science and the means by which machines are trained to learn and reason. Inspired by how Big Tech leaders describe unsupervised machine learning, and the co-optation of “revolutionary” rhetoric they use to do so, we introduce a concept we call “techniques of invisibility.” Techniques of invisibility are the ways in which an extreme imbalance between exposure and opacity, demarcated along fault lines of power, are fabricated and maintained, closing down the possibility for bidirectional transparency in the production and applications of algorithms. Further, techniques of invisibility, which we group into two categories—epistemic injustice and the Brotherhood—include acts of subjection by powerful actors in data science designed to quell resistance to exploitative relations. These techniques may be useful in making further connections between epistemic violence, sexism, and surveillance, sussing out persistent boundary violations in data science to render the social in data science visible, and open to scrutiny and debate.
      Citation: Big Data & Society
      PubDate: 2021-08-17T07:39:50Z
      DOI: 10.1177/20539517211035673
      Issue No: Vol. 8, No. 2 (2021)
  • Phenotyping as disciplinary practice: Data infrastructure and the
           interprofessional conflict over drug use in California

    • Authors: Mustafa I Hussain, Geoffrey C Bowker
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      The narrative of the digital phenotype as a transformative vector in healthcare is nearly identical to the concept of “data drivenness” in other fields such as law enforcement. We examine the role of a prescription drug monitoring program in California—a computerized law enforcement surveillance program enabled by a landmark Supreme Court case that upheld “broad police powers”—in the interprofessional conflict between physicians and law enforcement over the jurisdiction of drug use. We bring together interview passages, clinical artifacts, and academic and gray literature to investigate the power relations between police, physicians, and patients to show that prescribing data appear to the physician as evidence of problematic patient behavior by the patients, and to law enforcement as evidence of physician misconduct. In turn, physicians have adopted a disciplinary approach to patients, using quasi-legalistic documents to litigate patient behavior. We conclude that police powers have been used to pave data infrastructure through a contested jurisdiction, and law enforcement have used that infrastructure to enroll physicians into the work of disciplining patients.
      Citation: Big Data & Society
      PubDate: 2021-08-16T09:14:56Z
      DOI: 10.1177/20539517211031258
      Issue No: Vol. 8, No. 2 (2021)
  • Of dog kennels, magnets, and hard drives: Dealing with Big Data

    • Authors: Zane Griffin Talley Cooper
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      How did the 3.5-inch Winchester hard disk drive become the fundamental building block of the modern data center' In attempting to answer this question, I theorize the concept of "data peripheries" to attend to the awkward, uneven, and unintended outsides of data infrastructures. I explore the concept of data peripheries by first situating Big Data in one of its many unintended outsides—an unassuming dog kennel in Indiana housed in a former permanent magnet manufacturing plant. From the perspective of this dog kennel, I then build a history of the 3.5-inch Winchester hard disk drive, and weave this hard drive history through the industrial histories of rare earth mining and permanent magnet manufacturing, focusing principally on Magnequench, a former General Motors subsidiary, and its sale and movement of operations from Indiana to China in the mid-1990s and early 2000s. I then discuss how mobilities of rare earths, both as materials and political discourse, shape Big Data futures, and conclude by speculating on how using the situated lenses of data peripheries (such as this Indiana dog kennel) can open up new methods for studying the material entanglements of Big Data writ large.
      Citation: Big Data & Society
      PubDate: 2021-08-08T10:41:05Z
      DOI: 10.1177/20539517211015430
      Issue No: Vol. 8, No. 2 (2021)
  • Design choices: Mechanism design and platform capitalism

    • Authors: Salomé Viljoen, Jake Goldenfein, Lee McGuigan
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      Mechanism design is a form of optimization developed in economic theory. It casts economists as institutional engineers, choosing an outcome and then arranging a set of market rules and conditions to achieve it. The toolkit from mechanism design is widely used in economics, policymaking, and now in building and managing online environments. Mechanism design has become one of the most pervasive yet inconspicuous influences on the digital mediation of social life. Its optimizing schemes structure online advertising markets and other multi-sided platform businesses. Whatever normative rationales mechanism design might draw on in its economic origins, as its influence has grown and its applications have become more computational, we suggest those justifications for using mechanism design to orchestrate and optimize human interaction are losing traction. In this article, we ask what ideological work mechanism design is doing in economics, computer science, and its applications to the governance of digital platforms. Observing mechanism design in action in algorithmic environments, we argue it has become a tool for producing information domination, distributing social costs in ways that benefit designers, and controlling and coordinating participants in multi-sided platforms.
      Citation: Big Data & Society
      PubDate: 2021-08-04T01:38:07Z
      DOI: 10.1177/20539517211034312
      Issue No: Vol. 8, No. 2 (2021)
  • Heritage transformations

    • Authors: Chiara Bonacchi
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      This special theme examines the dynamic relationships between production, availability, and usage of Big Data, laying out a research agenda for digital heritage at the time of the ‘data turn’. Over the past 15 years, a proliferation of heritage data has been generated by ‘ecosystems of distributed practices’ enacted by the co-working of bodies, cultural identities, organisational workflows, software, application programming interfaces, etc. The authors of research articles and commentaries in this collection explore the three macro-dimensions along which we can map transformations of and by heritage in Big Data ecologies: (a) ontologies or heritage as datified resources, (b) interactions and (c) methodologies and epistemologies.
      Citation: Big Data & Society
      PubDate: 2021-08-04T01:36:38Z
      DOI: 10.1177/20539517211034302
      Issue No: Vol. 8, No. 2 (2021)
  • In search of ‘extra data’: Making tissues flow from personal
           to personalised medicine

    • Authors: Clémence Pinel, Mette N Svendsen
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      One of the key features of the contemporary data economy is the widespread circulation of data and its interoperability. Critical data scholars have analysed data repurposing practices and other factors facilitating the travelling of data. While this approach focused on flows provides great potential, in this article we argue that it tends to overlook questions of attachment and belonging. Drawing upon ethnographic fieldwork within a Danish data-linkage infrastructure, and building upon insights from archival science, we discuss the work of data practitioners enabling the repurposing of pathology samples extracted from patients for the conduct of ‘personal medicine’ – our term to discuss the so-called old-fashioned treatment of patients – towards personalised medicine. This first involves ‘getting to know’ the tissues and unpacking their previous uses and meanings, then detaching them from their original source to extract data from such tissues and making them flow towards a new container where they can be worked on and connected with other data. As data practitioners make these tissues travel, transforming them into research data, they organise the attachments of data to new agendas, persons and places. Crucially, in our case, we observe the prominence of national attachments, whereby managing tissues and data in and out of containers involves tying them to the nation to serve its interests. We thus expose how the building of data linkage infrastructures entails more than the accumulation and curation of data, but also involves crafting meanings, futures and belonging to specific communities and territories.
      Citation: Big Data & Society
      PubDate: 2021-08-02T02:44:23Z
      DOI: 10.1177/20539517211035664
      Issue No: Vol. 8, No. 2 (2021)
  • Corrigendum to Racial formation, coloniality, and climate finance
           organizations: Implications for emergent data projects in the Pacific

    • Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.

      Citation: Big Data & Society
      PubDate: 2021-07-22T03:34:36Z
      DOI: 10.1177/20539517211034695
      Issue No: Vol. 8, No. 2 (2021)
  • Reading datasets: Strategies for interpreting the politics of data

    • Authors: Lindsay Poirier
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities they produce. In this article, I introduce three modes of reading that can be engaged when studying datasets—a denotative reading (extrapolating the literal meaning of values in a dataset), a connotative reading (tracing the socio-political provenance of data semantics), and a deconstructive reading (seeking what gets Othered through data semantics and structure). I then outline how I have taught students to engage these methods when analyzing three datasets in Data and Society—a course designed to cultivate student competency in politically aware data analysis and interpretation. I show how combined, the reading strategies prompt students to grapple with the double binds of perceiving contemporary problems through systems of representation that are always situated, incomplete, and inflected with diverse politics. While I introduce these methods in the context of teaching, I argue that the methods are integral to any data practice in the conclusion.
      Citation: Big Data & Society
      PubDate: 2021-07-02T06:46:40Z
      DOI: 10.1177/20539517211029322
      Issue No: Vol. 8, No. 2 (2021)
  • Algorithmic management in a work context

    • Authors: Mohammad Hossein Jarrahi, Gemma Newlands, Min Kyung Lee, Christine T. Wolf, Eliscia Kinder, Will Sutherland
      Abstract: Big Data & Society, Volume 8, Issue 2, July-December 2021.
      The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.
      Citation: Big Data & Society
      PubDate: 2021-07-02T06:46:37Z
      DOI: 10.1177/20539517211020332
      Issue No: Vol. 8, No. 2 (2021)
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