Jump to content

Social data science

Add links
From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Karohusb (talk | contribs) at 12:31, 21 August 2023. The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.


Definition

Social data science (SDS) is an interdisciplinary field that addresses social science problems by applying or designing computational and digital methods. The data in SDS is always about human beings and derives from social phenomena, and it could be structured data (e.g. surveys) or unstructured data (e.g. social media text). The goal of SDS is to yield new knowledge about social networks, human behavior, cultural ideas and political ideologies. A social data scientist combines domain knowledge and specialized theories from the social sciences with programming, statistical and other data analysis skills.

Methods

Overview

SDS employs a wide range of quantitative and qualitative methods - both established methods in social science as well as new methods developed in computer science and interdisciplinary data science fields such as natural language processing (NLP) and network science. SDS is closely related to Computational Social Science, but also sometimes includes qualitative research and mixed methods [1] [2] [3] [4] [5] [6] [7] [8]

Common SDS methods include:

Qualitative methods:

Quantitative methods:

Mixed methods:

One of the pillars of social data science is in the combination of qualitative and quantitative data to analyze social phenomena and develop computationally grounded theories [23] [24] [25] [26] . For example by using mixed digital methods to digitize qualitative data and analyzing it via computational methods, or by qualitatively analyzing and interpreting quantitative data.

Data

Social data scientists use both digitized data [27] (e.g. old books that have been digitized) and natively digital data (e.g. social media posts) [28] [29]. Since such data often take the form of found data that were originally produced for other purposes (commercial, governance, etc.) than research, data scraping, cleaning and other forms of preprocessing and data mining occupy a substantial part of a social data scientist’s job.

Sources of SDS data include:

  • Text data
  • Sensor data
  • Register data
  • Survey data
  • Geo-location data
  • Observational data

Relations to other fields

Social Sciences

SDS is part of the social sciences along with established disciplines (anthropology, economics, political science, psychology, and sociology) and newer interdisciplinary fields like behavioral science, criminology, international relations, and cognitive science. As such, its fundamental unit of study is social relations, human behavior and cultural ideas, which it investigates by using quantitative and/or qualitative data and methods to develop, test and improve fundamental theories concerning the nature of the human condition [30]. SDS also differs from traditional social science in two ways. First, its primary object science is digitized phenomena and data in the widest sense of this word, ranging from digitized text corpora to the footprints gathered by digital platforms and sensors [31] [32]. Secondly, more than simply applying existing quantitative and qualitative social science methods, SDS seeks to develop and disrupt these via the import and integration of state of the art of data science techniques [33] [34] [35].

Data Science

SDS is a form of data science in that it applies advanced computational methods and statistics to gain information and insights from data [36] [37]. SDS researchers often make use of methods developed by data scientists, such as data mining and machine learning, which includes but is not limited to the extraction and processing of information from big data sources. Unlike the broader field of data science, which involves any application and study involving the combination of computational and statistical methods, SDS mainly concerns the scientific study of digital social data and/or digital footprints from human behavior.

Computational Social Science

Like computational social science, SDS uses data science methods to solve social science problems. This includes the reappropriation and refinement of methods developed by data scientists to better fit the questions and data of the social sciences as well as their specialized domain knowledge and theories [38] [39]. Unlike computational social science, SDS also includes critical studies of how digital platforms and computational processes affect wider society and of how computational and non-computational approaches integrate and combine.

Digital Methods

While most SDS researchers are close affiliated with or part of computational social science, some qualitative oriented social data scientists are influenced by the fields of digital humanities and digital methods [40] [41] that emerged from science and technology studies (STS). Like digital methods, the aim is here to repurpose the ‘methods of the medium’ to study digitally-mediated society and to engage in an ongoing discussions about bias in science and society by bringing computational social science and Digital Methods into dialogue. SDS is also related to digital sociology[42] and digital anthropology[43], but to a higher degree aspires to augment qualitative data and digital methods with state of the art data science techniques.

References

  1. ^ Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723
  2. ^ Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  3. ^ Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1.
  4. ^ Pentland, A. (2015) Social Physics: How Social Networks Can Make Us Smarter. London: Penguin.
  5. ^ Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2020). Big data and social science: data science methods and tools for research and practice. CRC Press.
  6. ^ Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press
  7. ^ Veltri, G. A. (2019). Digital social research. John Wiley & Sons.
  8. ^ Nelimarkka, M. (2022). Computational Thinking and Social Science: Combining Programming, Methodologies and Fundamental Concepts. London: Sage
  9. ^ Nippert-Eng, C. (2015). Watching Closely: A Guide to Ethnographic Observation. Oxford University Press.
  10. ^ Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press
  11. ^ Ford, H. (2014) Big data and small: Collaborations between ethnographers and data scientists. Big Data & Society 1(2): 205395171454433.
  12. ^ Blok, A. and Pedersen, M.A. (2014). Complementary social science? Quali-quantitative experiments in a Big Data world. Big Data & Society 1(2): 1–6.
  13. ^ Munk, A.K. (2019). Four styles of quali-quantitative analysis: making sense of the new Nordic food movement on the web. Nordicom Review 40(1): 159–176
  14. ^ Moats, D. & Borra, E. (2018) Quali-quantitative methods beyond networks: Studying information diffusion on twitter with the modulation sequencer. Big Data & Society 5(1): 205395171877213.
  15. ^ Isfeldt, A.S., Enggaard, T.R., Blok, A., Pedersen, M.A. (2022). Grøn Genstart: A quali-quantitative micro-history of a political idea in real-time. Big Data & Society 9 (1)
  16. ^ Beaulieu, A. (2017) Vectors for fieldwork: Computational thinking and new modes of ethnography. In: Hjorth, L., Horst, H., Galloway, A., et al. (eds) The Routledge Companion to Digital Ethnography. London: Routledge, pp.55–65.
  17. ^ Munk, A.K., Winthereik, B.R. (2022). Computational Ethnography: A Case of COVID-19’s Methodological Consequences. In: Bruun, M.H., et al. The Palgrave Handbook of the Anthropology of Technology. Palgrave Macmillan, Singapore
  18. ^ Breslin, S., et al. 2022. Affective Publics: Performing Trust on Danish Twitter during the COVID-19 Lockdown”. Current Anthropology 63(2).
  19. ^ Paff, S. (2021). Anthropology by Data Science. Annals of Anthropological Practice 46 (1): 7- 18.
  20. ^ Santucci, J-F., Doja, A. & Capocchi, L. (2020) A discrete-event simulation of Claude Lévi-Strauss’ structural analysis of myths based on symmetry and double twist transformations. Symmetry 12(10): 1706.
  21. ^ Munk, A.K., Olesen, A.G., & Jacomy, M. (2022). The Thick Machine: Anthropological AI between explanation and explication. Big Data & Society, 9(1).
  22. ^ Pedersen, M.A. (2023). Editorial introduction: Towards a machinic anthropology. Big Data & Society, 10(1).
  23. ^ Small, M.L. (2011). How to Conduct a Mixed Methods Study: Recent Trends in a Rapidly Growing Literature. Annual Review of Sociology 37:1, 57-86
  24. ^ Nelson, L.K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research 49(1): 3-42.
  25. ^ Nelson, L.K., Burk, D., Knudsen, M., et al. (2021) The future of coding: A comparison of hand-coding and three types of computer-assisted text analysis methods. Sociological Methods & Research 50(1): 202–237.
  26. ^ Carlsen, H.B. & Ralund, S. (2022). Computational Grounded Theory Revisited. Big Data and Society 9 (1).
  27. ^ Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267-297.]
  28. ^ Kramer, A.D., Guillory, J.E., & Hancock, J.T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National academy of Sciences of the United States of America, 111(24), 8788.]
  29. ^ https://www.nature.com/collections/cadaddgige
  30. ^ Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons.
  31. ^ Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  32. ^ Veltri, G. A. (2019). Digital social research. John Wiley & Sons.
  33. ^ Lazer D, Hargittai E, Freelon D, et al. (2021) Meaningful measures of human society in the twenty-first century. Nature 595(7866): 189–196.
  34. ^ Rahwan, I., Cebrian, M., Obradovich, N. et al. (2019) Machine behaviour. Nature 568, 477–486. https://doi.org/10.1038/s41586-019-1138-y
  35. ^ Pedersen, M. A. Eds. (2023). Machine Anthropology. Special issue of Big Data & Society, 10(1).
  36. ^ King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331(6018), 719-721.
  37. ^ Giles, J. (2012). Computational social science: Making the links. Nature, 488(7412), 448-450.
  38. ^ Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723
  39. ^ Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1.
  40. ^ Ruppert E, Law J, Savage M (2013) Reassembling social science methods: The challenge of digital devices. Theory, Culture & Society 30(4): 22–46.
  41. ^ Rogers R (2019) Doing Digital Methods. North Tyneside: SAGE.
  42. ^ Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons.
  43. ^ Varis, P. (2015). Digital ethnography. In The Routledge handbook of language and digital communication (pp. 55-68). Routledge.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Social_data_science&oldid=1171492959"