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Does ‘bigger’ mean ‘better’? Pitfalls and shortcuts associated with big data for social research

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Abstract

‘Big data is here to stay.’ This key statement has a double value: is an assumption as well as the reason why a theoretical reflection is needed. Furthermore, Big data is something that is gaining visibility and success in social sciences even, overcoming the division between humanities and computer sciences. In this contribution some considerations on the presence and the certain persistence of Big data as a socio-technical assemblage will be outlined. Therefore, the intriguing opportunities for social research linked to such interaction between practices and technological development will be developed. However, despite a promissory rhetoric, fostered by several scholars since the birth of Big data as a labelled concept, some risks are just around the corner. The claims for the methodological power of bigger and bigger datasets, as well as increasing speed in analysis and data collection, are creating a real hype in social research. Peculiar attention is needed in order to avoid some pitfalls. These risks will be analysed for what concerns the validity of the research results ‘obtained through Big data. After a pars distruens, this contribution will conclude with a pars construens; assuming the previous critiques, a mixed methods research design approach will be described as a general proposal with the objective of stimulating a debate on the integration of Big data in complex research projecting.

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Notes

  1. The book title, not by chance, is: “Taming the tidal wave of Big data”.

  2. Internet World Stats, usage and population statistics, checked on June the 5th 2014.

  3. Comparison between Internet use in households and by individuals edition 2009 and 2012; data coming from Eurostat: statistics in focus.

  4. Or five if we include value and veracity (Demchenko et al. 2013), but in the interests of economy they will not be accounted for in this paper.

  5. According to Hilbert and López (2011), Communication capacity is certainly growing but they admit that still “(...) the digital age increased our capacity to store information [...] much more than our capacity to transmit information through broadcast and telecommunication networks.” Anyway this element will be further analysed later on as one of the main criticalities of BD.

  6. In the chapter in his edited book, Bijker analysed the fluorescent lamp; later on he dealt with bakelites, bicicles and incadescent bulbs (1997).

  7. http://google.com/patents/US6317722, Retrieved on February the 12th, 2014.

  8. An attempt to apply ANT approach to the description of the raise of electronic market in the case of EUREX has been conducted in Baygeldi and Smithson (2004). The ability of Actor Network Theory (ANT) to model and interpret an electronic market. Creating Knowledge-based Organisations, 109–26.

  9. Or ‘Ecology’ as meant by Abbott (2005). In contrast with deterministic concepts such as mechanisms and organisms, he argued that “When we call a set of social relations an ecology, we mean that it is best understood in terms of interactions between multiple elements that are neither fully constrained nor fully independent.” p. 248.

  10. As defined by Internet Engineering Task Force (IETF) of Berkeley University in the document available on http://tools.ietf.org/html/rfc6265#section-3. Checked on August 9th 2014.

  11. Eric Schimdt’s speech to the Association of National Advertisers, 2005, retrieved on Google press on May 21 2014. http://www.google.com/press/podium/ana.html.

  12. Big Data: the Management revolution, McAfee and Brynjolfsson in Big data’s management revolution, The Promise and Challange of Big Data, Harvard Business Review Insight Center Report, September 11, 2014.

  13. In computer programming Application Programming Interface, is exactly a software interface able to regulate software interaction. In this case, APIs are useful interfaces that allow interaction between different web applications as for instance social networks sites.

  14. Ginsberg et al. (2009) Detecting influenza epidemics using search engine query data, Nature vol. 457, 19 February 2009.

  15. Actually Rogers quotes Mike Thelwall, an affirmed webometrician, who said that the challenge “is to demonstrate the web data correlate significantly with some non web data in order to prove that web data are not wholly random” (p. 205).

  16. A global map produced by Eric Fisher based on Twitter is available here: https://www.mapbox.com/labs/twitter-gnip/locals/#.

  17. The celeb way of labelling a content in order to assure that the content is referred to a specific object, topic or event.

  18. Since 2010, on Sage’s Social Science Computer Review, 53 papers (57.6 % of total) has been published basing their empirical data on Twitter. Site consulted last time on 17th July 2014. As Zeynep Tufekci noticed (2014), at the 2013 edition of International Conference of Weblogs and Social Media (ICWSM) almost more than thirty papers (the half of total accepted) were based on some kind of Twitter analysis.

  19. It should be mark out that computer sciences applied on content analysis is proceeding very fast. Information retrieval is a cutting edge field of research for informatics: some of the key articles are about machine learning and sentiment analysis. The former is Blei et al. (2003), on the so called LDA (Latent Dirichelet Allocation) that allows to automatically individuate on probabilistic basis the topics contained into a complex corpus of texts; the latter is Pang and Lee (2008), about sentiment analysis, a way to automatically calculate partisanship of texts about a certain topic.

  20. A pure mix of social sciences and informatics might probably be a new frontier for high education but this would open an other kind problems in the academic world.

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  1. Department of Economy, Society and Politics (DESP), University of Urbino Carlo Bo, Via Saffi, 15-61029, Urbino, Pesaro-Urbino, Italy

    Paolo Giardullo

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Giardullo, P. Does ‘bigger’ mean ‘better’? Pitfalls and shortcuts associated with big data for social research. Qual Quant 50, 529–547 (2016). https://doi.org/10.1007/s11135-015-0162-8

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