SOCIAL SCIENCES AND HUMANITIES ON BIG DATA: A BIBLIOMETRIC ANALYSIS

Becerra, Gastón; Ratovicius, Cristian

doi:10.4301/S1807-1775202219011

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Original Article • JISTEM J.Inf.Syst. Technol. Manag. 19 • 2022 • https://doi.org/10.4301/S1807-1775202219011 copy

SOCIAL SCIENCES AND HUMANITIES ON BIG DATA: A BIBLIOMETRIC ANALYSIS

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT The purpose of this paper is to provide a comprehensive bibliometric review of social science, psychology, and humanities literature focusing on big data. Methods: Production and authorship trends, topics and areas as well as citations were analyzed by means of conducting a bibliometric analysis of a corpus of 5,500 Scopus articles published from 2010 to 2020. Findings: Analysis revealed similarities and differences among social science, psychology, and humanities literature in terms of publication, framing, and referencing trends as compared with the general big data literature: both fields show a steady increase, although the increase rate slowed down as from 2015; text production of both specific and general fields is led by just a few countries, with the USA and China being on top of the ranking; single authorship has been decreasing in both fields; the specificity of big data framing, in social sciences and humanities, has been identified with a critical view that surpass the ethical considerations, to include the social construction of datasets, the political and ideological uses of big data, and the discussion of its philosophical and epistemological foundations. Value: To the best of our knowledge, this is the first study to provide a comprehensive view on social sciences and humanities big data bibliometrics while providing context to compare results.

Keywords:
Big data; social sciences; humanities; bibliometric analysis; citation analysis

Paper	Corpus	Period	Source	Search.criteria
Ahmad, 2020Ahmad, I., Ahmed, G., Shah, S. A. A., & Ahmed, E. (2020). A decade of big data literature: analysis of trends in light of bibliometrics. The Journal of Supercomputing, 76(5), 3555-3571. https://doi.org/10.1007/s11227-018-2714-x https://doi.org/10.1007/s11227-018-2714-...	33,623	2008-2017	Scopus	"big data"; not limited to computer sciences
Liu, 2019Liu, X., Sun, R., Wang, S., & Wu, Y. J. (2019). The research landscape of big data: a bibliometric analysis. Library Hi Tech, 38(2), 367-384. https://doi.org/10.1108/LHT-01-2019-0024 https://doi.org/10.1108/LHT-01-2019-0024...	4,070	2013-2018	Scopus	big data"
Kalantari, 2017Kalantari, A., Kamsin, A., Kamaruddin, H. S., Ale Ebrahim, N., Gani, A., Ebrahimi, A., & Shamshirband, S. (2017). A bibliometric approach to tracking big data research trends. Journal of Big Data, 4(1), 1-18. https://doi.org/10.1186/s40537-017-0088-1 https://doi.org/10.1186/s40537-017-0088-...	6,572	1980-2015	WoS	several keywords defined by expert surveying
Zhang, 2019Zhang, Y., Huang, Y., Porter, A. L., Zhang, G., & Lu, J. (2019). Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study. Technological Forecasting and Social Change, 146(April), 795-807. https://doi.org/10.1016/j.techfore.2018.06.007 https://doi.org/10.1016/j.techfore.2018....	5,840	2000-2015	WoS	search keywords from technologies (e.g., "big data", "map reduce")
Belmonte, 2020Belmonte, J. L., Segura-Robles, A., Moreno-Guerrero, A. J., & Parra-González, M. E. (2020). Machine learning and big data in the impact literature. A bibliometric review with scientific mapping in web of science. Symmetry, 12(4). https://doi.org/10.3390/SYM12040495 https://doi.org/10.3390/SYM12040495...	4,240	2010-2019	WoS	"big data" and "machine learning"
Raban, 2020	7,786	2010-2019	WoS	"big data" and "datascience"
Liang, 2018Liang, T. P., & Liu, Y. H. (2018). Research Landscape of Business Intelligence and Big Data analytics: A bibliometrics study. Expert Systems with Applications, 111(128), 2-10. https://doi.org/10.1016/j.eswa.2018.05.018 https://doi.org/10.1016/j.eswa.2018.05.0...	10,637	1990-2017	SSCIE	"big data"

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Own elaboration	-	0.00	600.00	319.05	205.68	34.57	57.73	33.45	19.29	34.98	4.73
Liu, et. al., 2019Liu, X., Sun, R., Wang, S., & Wu, Y. J. (2019). The research landscape of big data: a bibliometric analysis. Library Hi Tech, 38(2), 367-384. https://doi.org/10.1108/LHT-01-2019-0024 https://doi.org/10.1108/LHT-01-2019-0024...	-	-	-	-	105.94	166.83	45.77	30.04	21.01	-	-
Belmonte, et. al., 2020Belmonte, J. L., Segura-Robles, A., Moreno-Guerrero, A. J., & Parra-González, M. E. (2020). Machine learning and big data in the impact literature. A bibliometric review with scientific mapping in web of science. Symmetry, 12(4). https://doi.org/10.3390/SYM12040495 https://doi.org/10.3390/SYM12040495...	-	0.00	1,400.00	286.67	203.45	100.00	55.40	43.51	46.75	1.22	-

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
1	0.33	0.67	0.48	0.44	0.40	0.39	0.33	0.30	0.25	0.25	0.21
2	0.33	-	0.29	0.31	0.24	0.26	0.24	0.26	0.25	0.26	0.26
3	0.33	-	0.10	0.17	0.18	0.15	0.18	0.16	0.19	0.20	0.21
4	-	-	0.05	0.03	0.10	0.09	0.11	0.12	0.15	0.14	0.15
5	-	-	-	0.02	0.04	0.06	0.05	0.07	0.07	0.07	0.08
6	-	-	-	0.01	0.01	0.03	0.03	0.04	0.04	0.04	0.04
7	-	0.33	-	0.01	0.00	0.01	0.02	0.02	0.01	0.02	0.03
8	-	-	-	-	0.00	0.01	0.01	0.01	0.01	0.01	0.01
9	-	-	0.05	-	0.00	0.00	0.01	0.01	0.01	0.00	0.01
10	-	-	-	-	0.01	0.01	0.00	0.01	0.00	0.00	0.00
+10	0.00	0.00	0.05	0.00	0.01	0.01	0.01	0.01	0.01	0.00	0.01
S.I.	0.67	0.33	0.52	0.56	0.60	0.61	0.67	0.70	0.75	0.75	0.79

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[1] Address for correspondence:
Gastón Becerra, Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina. E-mail: gaston.becerra@gmail.com; Cristian Ratovicius, Universidad Abierta Interamericana, Facultad de Tecnología Informática. Centro de Altos Estudios en Tecnología Informática. Buenos Aires, Argentina. E-mail: cratovicius@yahoo.com