To promote scholar exchange around data studies, BD&S is holding a series of online webinar panel discussions with experts (drawn from our editors and authors) from around the globe. Everyone is welcome to attend.
Tuesday, 9 August 2022
Monday, 20 June 2022
Thursday, 16 June 2022
‘Real-time’ air quality channels: A technology review of emerging environmental alert systems by Kayla Schulte
Kayla Schulte introduces a new paper on, "‘Real-time’ air quality channels: A technology review of emerging environmental alert systems", out in Big Data & Society doi:10.1177/20539517221101346. First published June 15, 2022.
Poor air quality is a pressing global challenge contributing to adverse health impacts around the world. In the past decade, there has been a rapid proliferation of air quality information delivered via sensors, apps, websites or other media channels in near real-time and at increasingly localized geographic scales. This paper explores the growing emphasis on self-monitoring and digital platforms to supply informational interventions for reducing pollution exposures and improving health outcomes at the individual level. It presents a technological case study that characterizes emerging air quality information communication mechanisms, or ‘AQ channels’, while drawing upon examples throughout the literature. The questions are posed: which air quality channels are ‘freely’ available to individuals in London, UK, and when and where are they accessed? Digital trace data and metadata associated with 54 air quality channels are synthesized narratively and graphically. Results reveal air quality channels derive air pollution estimates using common data sources, display disparate messaging, adopt variable geographic scales for reporting ‘readings’ and maintain psychosocial barriers to access and adoption of exposure-reducing behaviours. The results also point to a clear association between the publication of a high-profile news article about air pollution and increased air quality channel access. These findings illuminate a need for greater transparency around how air quality channels generate personalized air pollution exposure estimates and tailor messaging. The paper concludes by calling for air quality channel developers to exercise co-creative methods that can support sustainable, democratic data and knowledge production around air quality, while critically approaching disproportionate patterns of both pollution and information exposure.
Tuesday, 14 June 2022
Wednesday, 25 May 2022
Call for Special Theme Proposals for Big Data & Society
The SAGE open access journal Big Data & Society (BD&S) is soliciting proposals for a Special Theme to be published in 2023/24. BD&S is the highest ranked journal in the Social Sciences Interdisciplinary category of the Social Sciences Citation Index (SSCI) with an impact factor of 5.987 in 2021. BD&S is a peer-reviewed, interdisciplinary, scholarly journal that publishes research about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business and government relations, expertise, methods, concepts and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practices that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government and crowd-sourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes.
Special Themes can consist of a combination of Original Research Articles (10,000 words; maximum 6), Commentaries (3,000 words; maximum 4) and one Editorial (3,000 words). All Special Theme content will be waived Article Processing Charges. All submissions will go through the Journal’s standard peer review process.
Past special themes for the journal have included: Knowledge Production; Algorithms in Culture; Data Associations in Global Law and Policy; The Cloud, the Crowd, and the City; Veillance and Transparency; Practicing, Materializing and Contesting Environmental Data; Spatial Big Data; Critical Data Studies; Social Media & Society; Assumptions of Sociality; Data & Agency; Health Data Ecosystems; Algorithmic Normativities; Big Data and Surveillance; The Turn to AI in Governing Communication Online; The Personalization of Insurance; Heritage in a World of Big Data; Studying the COVID-19 Infodemic at Scale; Digital Phenotyping; Machine Anthropology; and Data, Power, and Racial Formation. See http://journals.sagepub.com/page/bds/collections/index for full listing
While open to submissions on any theme related to Big Data we particularly welcome proposals related to racialisation, indigenous data, health and education.
Format of Special Theme Proposals
Researchers interested in proposing a Special Theme should submit an outline with the following information.
An overview of the proposed theme, how it relates to existing research and the aims and scope of the Journal, and the ways it seeks to expand critical scholarly research on Big Data.
A list of titles, abstracts, authors and brief biographies. For each, the type of submission (ORA, Commentary) should also be indicated. If the proposal is the result of a workshop or conference that should also be indicated.
Short Bios of the Guest Editors including affiliations and previous work in the field of Big Data studies. Links to homepages, Google Scholar profiles or CVs are welcome, although we don’t require CV submissions.
A proposed timing for submission to Manuscript Central. This should be in line with the timeline outlined below.
Information on the types of submissions published by the Journal and other guidelines is available at https://journals.sagepub.com/author-instructions/BDS .
Timeline for Proposals
Please submit proposals by August 15, 2022 to the Managing Editor of the Journal, Prof. Matthew Zook at email@example.com. The Editorial Team of BD&S will review proposals and make a decision by October 2022. Manuscripts would be submitted to the journal (via manuscript central) by or before January/February 2023. For further information or discuss potential themes please contact Matthew Zook at firstname.lastname@example.org.
Wednesday, 27 April 2022
Hey Siri, can you hear me now? A framework for building natural language processing tools that advance linguistic justice
The concept of linguistic justice can be used to frame NLP tool development in a way that
centres the needs of all users, rather than prioritising speakers of privileged languages like “Standard” English. Linguistic justice is achieved when all individuals are granted equitable access to social, political, and economic life, regardless of their linguistic repertoire. Linguistic justice, then, requires that NLP tools serve diverse speakers and signers equitably.
Our commentary examines in detail two issues with current NLP tool development. First, if NLP tools learn from datasets that lack sufficient data from speakers of minoritised language varieties, those tools may underperform for those users. Secondly, NLP systems can use language to infer information about the identities of users - a process known as linguistic profiling. Even when protected information (e.g., race, gender) is not directly provided to an NLP system, the system may still infer a users’ identity from features of their language use. Inferred characteristics may then be used to mediate access to goods, services, and opportunities, resulting in unlawful discrimination.
We present nine specific actions that researchers, developers, and business leaders can take to design, develop, and manage NLP systems that advance linguistic justice. This includes, for example, working with diverse language communities in participatory and empowering ways, ensuring language data is labeled by people familiar with the particular language variety, and examining and altering power structures so that the needs and perspectives of those at the margins are prioritised.
Instead of being comfortable with the status quo, this work requires imagining and working towards a world where users of all language varieties are able to equitably access social, economic, and political life. It requires rethinking how we collect data and what data we value in NLP development. Our nine actions provide a path forward toward that world – whereby NLP systems can advance linguistic justice and thereby, social justice.
Tuesday, 19 April 2022
During an initial analysis of empirical material collected to map the Norwegian public sector, we were struck by the discursive absence of citizens in the realisation of this all-encompassing administrative reform. This sparked our curiosity, leading us to raise our gaze from the organisational to the system level and investigate who was invited to participate in policy formation and how citizens were described, in both the resulting documents and practitioner discourse.
The Norwegian data-driven context is particularly interesting to investigate. The state has collected vast amounts of data on the population for decades, the recirculation of which can make data-driven public administration realisable on a scale unimaginable in many other countries. Norway is also characterised by a corporative pluralism where collaboration with externals and inter-dependent decision making with interest organisations and business representative organisations, is deemed fundamental to policy making (Rokkan, 1966).
Unexpectedly, we identified a paternalistic and top-down technocratic approach to citizen engagement with non-participation particularly apparent at the policy making level. Citizens and civil society are reduced to a passive but demanding ‘user’ to be served by the public sector. This is in direct contrast to active engagement with the private sector during all phases - from policy production through to implementation.
Datafication often escapes democratic decision-making as the context, values, and agendas of this administrative reform are obscured from citizens and civil society. We hope this paper sparks interest among practitioners and scholars alike.