Call for Papers: NORDICOM Review Special Issue
CALL FOR PAPERS: NORDICOM Review/ (OPEN ACCESS)
Making sense of small and big data as onlife traces
Special issue editors: Anja Bechmann, Associate Professor at Aarhus University & Visiting Assoc. Professor at UCI and Kjetil Sandvik, Associate Professor at University of Copenhagen.
Onlife designates the transformational reality that in contemporary developed societies, our offline and online experiences and lives are inextricably interwoven (Simon & Ess 2015). Our onlives produce digital traces or footprints, some of which are produced before birth by our parents and continue to exist even after our death (in the shape of registers, bank accounts, social media profiles etc.). This special issue of Nordicom Review examines and discusses how we create meaning in and make sense of small and big data as onlife traces. The fields of qualitative and quantitative studies are often depicted as each other’s counterpart. Yet, when researchers study onlife as a way of conceptualizing the digital layer of our lives, we meet similar obstacles. One of the major questions is how can we infer meaning from the digital traces made by the user to the actual use/praxis or partial to the human(s) behind (incentives, motives, needs)? This is a classical methodological question within communication and behavioural studies that has a renewed interest in the digital social sciences as we discover methodological trajectories into the onlife, be it studies of Internet of Things, apps (e.g. social media, games, self-trackers), or other digital communication and behaviours as traces of digital sociology.
Both small and big data studies approaches have tried to ‘solve’ this problem of inference by suggesting triangulation as methodological approach. Within big data studies more data on more users, multiple data points or data over a longer time span on the same user are used to create more ‘clear signals’ and to strengthen the predictions of the motives, ideologies, incentives, and needs of a specific user. Qualitative studies use for instance multisided ethnography and methodological triangulation to heighten the validity of findings when it comes to clearly understand the user and the use behind onlife traces.
In a time where user data have become sharable and tradable commodities and where governments, media, health and financial sectors build actions and decisions on top of predictions inferred from data traces of human communication and behaviour in digital spaces it is maybe more important than ever to understand and discuss not if we can make a solid one-to-one interpretation, but how we can advance our inferences or at least explicitly discuss in various studies how we have solved or coped with this issue by advancing our research questions, our methodological approaches, philosophical background or conceptual understanding.
We are interested in papers that study onlife empirically and – in this connection – discuss the methods used to infer meaning from data traces to the usage or users behind. Furthermore, we are looking for empirical studies that use and discuss methods to extract meaningful (sociological) findings out of small and big data and discuss the methodological challenges in doing so. We are also looking for philosophical and theoretical contribution (on e.g. AI reasoning) and historical contribution tracking methodological or theoretical conceptualizations of ‘meaning’ and sense-making in combination with traces of use within the framework of understanding digital traces in a sociological perspective. Furthermore, we encourage ethnographic papers studying developers’ work processes when extracting meaning from massive data and acting on top of such predictions. Contributions are not expected to ‘solve’ the ‘meaning’ problem, but by sampling a selection of papers that explicitly discuss this issue in various empirical contexts we hope the special issue can point to further directions in the field of digital sociology. As such, contributors are urged to relate their proposals to the concepts of meaning- and sense-making taking into account that meaning-making is more than a methodological, theoretical and philosophical concept: it is related to how we empirically propose understandings of big as well as small data.
Topics may include but are not limited to:
- empirical studies of digital traces within a sociological framework
- empirical or theoretical studies on transmissions from user data to actual usage
- empirical studies of cross-platform usage as indicators of digital user practices
- empirical studies or theoretical studies of representation in big data analysis and relations between big data and small data
- empirical studies of algorithms and media platform usage
- theoretical and methodological discussions on digital sociology, e.g. (lack of) context sensitivity in machine learning (e.g. neural networks or clustering models) applied to digital sociology
- historical accounts of ‘meaning’ and ‘reasoning’ in AI, and of ‘meaning’ and ‘sense-making’ in qualitative studies within digital sociology
- human-machine communication studies of bots
- STS studies of developers’ work processes with big data and machine learning; critical discussions on internet profiling, algorithms and AI; auditing studies of machine learning algorithms; ontologies and (lack of) classifiers in big data and associated machine learning methods
1 September 2017: Deadline for abstracts (max. 500 words, excl. references, and additionally a short author bio, max. 150 words). Send submissions to: Anja and Kjetil with the text “Abstract proposal for Nordicom Special Issue 2018” in the subject line.
1 November 2017: Notification of authors.
1 April 2018: Deadline for full papers (7000 words).
1 June 2018: reviewers’ comments for the authors
1 September 2018: Revised versions from the authors
Preliminary date for publication: Winther 2018/2019
All full papers will undergo double-blind peer review, and acceptance of an abstract does not guarantee publication.
For questions regarding Nordicom Review, contact: Ingela Wadbring.