Data practices in Computational Social Science. A scoping review

Abstract

Aided by the technological progress of the past decades, the age of "computational everything"

is upon us. Computational sub-fields are emerging in disciplines across the board, from

biology to humanities, and everything in between. Computational social science is no exception,

with the term recording an exponential boom in literature usage starting around year 2000,

according to Google Ngrams. But what is computational social science? And more importantly,

what are the methodological, data, and ethical practices associated with the research

literature that uses the composite term "computational social science" as a keyword. This

mapping exercise is not only informative as a snapshot for the current state of the field, but

more importatly, it enables us to identify and critically assess the data practices that

underpin the coming together of priorly distinct research traditions. We place the emphasis of

our inquiry onto data practices in research built on data pertaining to human subjects,

narrowing in on both methodological approaches and ethical considerations. We expect the

relationship between research built on data pertaining to human subjects and the ethical

considerations under which such research is developed to be mediated by a number of factors,

such as territorial jurisdictions (eg. GDPR, or Bundesdatenschutzgesetz, just to name a few),

or ethical codes of conduct that regulate research practices at universities. Our proclivity

towards analyzing specifically research practices on data pertaining to human subjects comes

from the need to grasp critically the extent to which social science research traditions can,

and/or should, embrace computational modes of processing increasingly large amounts of data

generated by human activity online or through personal devices and technologies. Searching

Scopus, we identified 849 publications that have used the term "computational social science"

in the period 1999-2023 as a keyword. The majority of the publications are indexed under the

subject computer science (35.1%), followed by social sciences (21.4%). Mathematics and

engineering together make a total of 15% of the publications, and we expect this to be where

the methodological innovations are springing. Other social science disciplines, as well as

business, make up the other 30% of the publications. From this corpus of literature spanning

across disciplines, a relatively small percentage engages directly with data pertaining to

human subjects. Conversely, the studies that do, are largely social media studies. This paper

is trying to illuminate what stands in between the obvious and the possible, with the obvious

being the application of computational approaches to social media data, and the possible being

applications of such approaches to potentially more consequential, yet sensitive, human

matters. We see computational social science methods, and the ethical considerations that play

into those, on the one hand as a potential building block at the forefront of coming

developments in working with data at scale to learn about people and societies, and on the

other hand as a critical alternative to the commodification of personal data in the service of

commercial interests. With data thirsty technologies (eg. AI) becoming (at least) discursively

ubiquitous, we assume a normative stance in attempting to outline ethical data practices that

serve, rather than hamper, human interests, both presently and in the long term.

Keywords: scoping review, computational social sciences, data practices, research methods, research ethics

Authors: Alexandra Florea*, Lea Biere, Ilona Horwath, Silvia Fierăscu

Affiliations: Universität Paderborn, Germany; West University Timișoara, Romania

Contribution: Poster presentation @ IC2S2, Copenhagen, July 2023

* corresponding author, alexandra.florea@uni-paderborn.de

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