Abstract
Today large amounts of data are available to use for research on human behaviour: social media, data from online social networks, vast amounts of digital text, sensory information from personal hand-held and other devices, information from search engine usage and other online services, etc. The industry that relies on collecting, combining, selling and analysing digital footprints for all kinds of purposes ranging from simple targeted advertising to risk assessments and mass surveillance is developing with lightning speed. (Van Es and Schafer, 2017). However, such data could increasingly be used to address larger societal issues of social interactions and relations, inequality, education, healthcare, political participation, and more. The advances in the use of such data in social sciences offer the possibility to answer questions that were beyond research in the past, and this new generation of large-scale, complex, and usually unstructured data requires new forms of data analysis and scientific applications. Some also suggest that as a consequence of the data revolution that we are already living in, a major paradigm shift in science is expected with far-reaching consequences to how research is conducted and knowledge is produced (Mayer-Schonberger and Cukier, 2013; Meyer and Schroeder, 2015). While the course of development in the data-driven industries and research seems to be unambiguous for the future in terms of its expected impact on business and how societies function in general, today the possibilities are still frequently overestimated by some ‘positivistic prophets’ – coming mostly from outside academia. In addition to presenting the main arguments of the papers in this section, the purpose of this editorial is to highlight a few of those issues and challenges that may shape the future of social sciences and of those who pursue in it, in relation to the new data landscape. After briefly elaborating on the definitions of Big Data, the focus will move to the question of epistemology; the changing dynamics among various fields of sciences; the new divides in access to data; and the main ideas behind the critical approach that social sciences might follow to find their right place in the puzzle.
Highlights
Today large amounts of data are available to use for research on human behaviour: social media, data from online social networks, vast amounts of digital text, sensory information from personal hand-held and other devices, information from search engine usage and other online services, etc
After briefly elaborating on the definitions of Big Data, the focus will move to the question of epistemology; the changing dynamics among various fields of sciences; the new divides in access to data; and the main ideas behind the critical approach that social sciences might follow to find their right place in the puzzle
Based on more than 1500 conference papers and articles, De Mauro (2015) and his co-authors defined four core areas that were found in most Big Data perspectives and definitions: (1) the nature of information; (2) technology, as the equipment for working with Big Data; (3) processing methods that go beyond the traditional statistical techniques; and (4) the impact that Big Data can have on our lives
Summary
The complex phenomenon described above is usually referred to as Big Data, it might be misleading because of an inevitable limitation of the concept to a more mechanic and data-centred approach. The literature on interpreting Big Data from a social science perspective is expanding fast, but we still miss a uniform definition (Borgman, 2015; Csepeli, 2015; Dessewffy and Láng, 2015; Kitchin, 2014; McFarland et al, 2015; Székely, 2015). It is by far no coincidence, since the evolution of Big Data has been too quick and disordered so far, characterised by rapid technological changes. From the perspective of social sciences it means that on the one hand, many old research questions could be approached anew from novel angles, but on the other hand, a whole new set of questions are begging to be addressed (McFarland et al, 2015)
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