Location: PSH (Professor Stuart Hall Building) - LG02,
Goldsmiths, University of London, Building 2
Campus Map Contributors:
- Anu Masso, ETH Zürich, Switzerland
- Andra Siibal, Univrsity of Tartu, Estonia
- Maris Männiste, Univrsity of Tartu, Estonia
Background: In this paper we aim to contribute to the discussions about the methodological shifts related to the arrival of big data era and the dangers of research biases due to the polarization of scientific community regarding their skills in big data methods and methodologies. Methodological issues related to big data have been previously studied mainly theoretically (Kitchin, 2014; Shah, Cappella, & Neuman, 2015; Housley et al., 2014) suggesting that emergence of large datasets have evoked shifts both in data analysis techniques, methods and methodologies. However, there are almost no systematic studies analysing how these methodological changes are expressed in practice, i.e. in empirical social media studies.
Objective:
In this study we aim to fill this gap, by making meta analyses of previously conducted empirical studies that have used large social media databases and finding out the data analysis techniques, methods and methodologies used in the studies.
Hypothesis: (1) We assume based on previous studies (Kitchin, 2014; Lewis, Zamith, & Hermida, 2013) that traditional manual methods are combined with computational techniques, rather than being replaced by those, facilitating traditional forms of interpretation and theory-building. However, we assume, that the proportion of the data-driven theory building approaches are increasing in time compared to descriptive empirist research. (2) We also suppose based on previous studies (Boyd & Crawford, 2012; Burrows & Savage, 2014) the existence of digital divide in the field of big data methodologies, creating both institutional and individual inequalities (in so that the top-tier and well-resourced universities have both better access and skills for research of big social media data). However, (3) based on previous studies (Shah et al., 2015), we assume, that methodological reflections (e.g. questions of data quality, validity of analysis, correctness of inference, ethics) are more common in studies conducted in transdiciplinary teams compared to individually conducted studies within single discipline. Furthermore, based on previous studies (Bello-Orgaz, Jung, & Camacho, 2016) we assume that the main problems and limitations the authors acknowledge have to do with access to data, privacy, streaming and online algorithms, data fusion and – visualisation.
Methods:
In this paper the systematic literature review is combined with quantitative meta analysis methods of published academic peer-reviewed articles. The sample consists of empirical studies using social media data as basis and qualifying the study as falling into broad category of big data methodology. Articles are analysed mainly quantitatively combining standardised category schema and open coding function. We operationalize the inequality of big data by coding the articles by formal characteristics (e.g. institutional affiliation), content-related qualities (techniques, methods, methodologies used) and level of critical reflection (e.g. data quality).
References: Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45.
Boyd, D., & Crawford, K. (2012). CRITICAL QUESTIONS FOR BIG DATA: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. http://doi.org/10.1080/1369118X.2012.678878
Housley, W., Procter, R., Edwards, A., Burnap, P., Williams, M., Sloan, L., … Greenhill, A. (2014). Big and broad social data and the sociological imagination: A collaborative response. Big Data & Society, 1(2). http://doi.org/10.1177/2053951714545135
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1). http://doi.org/10.1177/2053951714528481
Kshetri, N. (2014). The emerging role of Big Data in key development issues: Opportunities, challenges, and concerns. Big Data & Society, 1(2). http://doi.org/10.1177/2053951714564227
Lewis, S., Zamith, R., & Hermida, A. (2013). Content Analysis in an Era of Big Data: A Hybrid Approach to Computational and Manual Methods. Journal of Broadcasting & Electronic Media, 57(1), 34–52. http://doi.org/10.1080/08838151.2012.761702
Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Big Data, Digital Media, and Computational Social Science. The ANNALS of the American Academy of Political and Social Science, 659(1), 6–13. http://doi.org/10.1177/0002716215572084
