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 14:52, 17 June 2024. The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Social data science is an interdisciplinary field that addresses social science problems by applying or designing computational and digital methods. As the name implies, Social Data Science is located primarily within the social science, but it relies on technical advances in fields like data science, network science, and computer science. The data in Social Data Science is always about human beings and derives from social phenomena, and it could be structured data (e.g. surveys) or unstructured data (e.g. digital footprints). The goal of Social Data Science is to yield new knowledge about social networks, human behavior, cultural ideas and political ideologies. A social data scientist combines cdomain knowledge and specialized theories from the social sciences with programming, statistical and other data analysis skills.

Methods

Social data science employs a wide range of quantitative - 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. Social Data Science is closely related to Computational Social Science, but also sometimes includes qualitative data, and mixed digital methods.

Common social data science methods include:

Quantitative methods:

Qualitative methods:

Mixed digital 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 [14][15]. For example by using mixed methods [16] to digitize qualitative data and analyzing it via computational methods, or by qualitatively analyzing and interpreting quantitative data [17].

Social Data Science is closely related to Computational Social Science, but also sometimes includes qualitative research and mixed digital methods[18] [19] [20] [21]

Data

Social data scientists use both digitized data [22] (e.g. old books that have been digitized) and natively digital data (e.g. social media posts) [23]. 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:

Relations to other fields

Social sciences

Social data science 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. SDS also differs from traditional social science in two ways.

  1. First, its primary object 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 [24] [25].
  2. Secondly, more than simply applying existing quantitative and qualitative social science methods, social data science seeks to develop and disrupt these via the import and integration of state of the art of data science techniques [26]

Data Science

Social data science is a form of data science in that it applies advanced computational methods and statistics to gain information and insights from data [27] [28]. Social data science 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, social data science mainly concerns the scientific study of digital social data and/or digital footprints from human behavior.

Computational Social Science

Like computational social science, social data science 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 [29][30]. Unlike computational social science, social data science 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 social data science researchers are closely 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[31][32] 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 [33] and digital anthropology, but to a higher degree aspires to augment qualitative data and digital methods with state of the art data science techniques.

History of the field

The origin of term “social data science” coincided with the emergence of a number of research centers and degree programs [34][35][36]. In 2016, the Copenhagen Center for Social Data Science (SODAS) - the first academic institution using the SDS name - was launched at the University of Copenhagen. The plan for an interdisciplinary center working at the intersection of the social and computational sciences was rooted in the Copenhagen Networks Study [37][38][39][40] from 2011-2016 by researchers from the Technical University of Denmark (DTU) and the University of Copenhagen. The University of Oxford and the University of Copenhagen were among the first research institutions to offer degree programmes in SDS. In 2018, the University of Oxford launched the one-year MSc in Social Data Science [41], which was followed by the two-year master’s programme at the University of Copenhagen in 2020 [42][43]. Since then, an increasing number of universities have begun to offer graduate programs or specializations in social data science

Social data science has emerged after the increasing availability of digitized social data, sometimes referred to as Big Data, and the ability to apply computational methods to this data at a low cost, which has offered novel opportunities to address questions about social phenomena and human behavior. While the origin of social data can be traced back to 1890s (when some 15 million individual records were processed by the US Census in the form of punch cards), the social data boom in the 21th century is a direct consequence of the increasing availability of consumer data resulting from the advent of e-commerce [44] Subsequent waves of availability of unstructured social data include Amazon.com review system and Wikipedia , and more recently, social media, which has played a key role in the emergence of the digital attention economy and big tech.

Criticism and debates

Data scientists have played a vital role in the data revolution, both during the original tech-optimist phase where big data and the Internet was seen as the solution to many societal and scientific problems, and as participants [45][46][47] in the tech-lash that followed in its wake as result of, among other things, the Facebook–Cambridge Analytica data scandal. Social data science researchers and research projects have been especially impactful in debates and criticism revolving around:

Impact and examples

Social data science research is typically published in multidisciplinary journals, including top general journals Science, Nature, and PNAS, as well as notable specialized journals such as:

In addition, social data science research is published in the top social science field journals including American Sociological Review, Psychological Science, American Economic Review, Current Anthropology.

Institutional status

Social data science activities are currently taking place in organisations such as

References

  1. ^ Grimmer, J., Roberts, M.E., & Stewart, B.M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press.
  2. ^ Nippert-Eng, C. (2015). Watching Closely: A Guide to Ethnographic Observation. Oxford University Press.
  3. ^ Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press
  4. ^ Ford, H. (2014). Big data and small: Collaborations between ethnographers and data scientists. Big Data & Society 1(2)
  5. ^ 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.
  6. ^ 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
  7. ^ Moats, D. and Borra, E. (2018) Quali-quantitative methods beyond networks: Studying information diffusion on twitter with the modulation sequencer. Big Data & Society 5(1).
  8. ^ 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)
  9. ^ 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.
  10. ^ 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
  11. ^ Paff, S. (2021). Anthropology by Data Science. Annals of Anthropological Practice 46 (1): 7- 18.
  12. ^ Munk, A.K., Olesen, A.G., & Jacomy, M. (2022). The Thick Machine: Anthropological AI between explanation and explication. Big Data & Society, 9(1).
  13. ^ Pedersen, M.A. Eds. (2023). Machine Anthropology. Special issue of Big Data & Society, 10(1).
  14. ^ Carlsen, H.B. & Ralund, S. (2022). Computational Grounded Theory Revisited. Big Data and Society 9 (1).
  15. ^ Nelson, L. K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research, 49(1), 3–42. https://doi.org/10.1177/0049124117729703
  16. ^ Mario Luis Small. How to Conduct a Mixed Methods Study: Recent Trends in a Rapidly Growing Literature. Annual Review of Sociology 2011 37:1, 57-86
  17. ^ Ford, H. (2014) Big data and small: Collaborations between ethnographers and data scientists. Big Data & Society 1(2): 205395171454433.
  18. ^ Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723
  19. ^ Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1.
  20. ^ Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press
  21. ^ Veltri, G.A. (2019). Digital social research. Polity Press.
  22. ^ 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.
  23. ^ 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.
  24. ^ Salganik, M.J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  25. ^ Veltri, G. A. (2019). Digital social research. Polity Press.
  26. ^ Cioffi-Revilla, C. (2010). Computational social science. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 259–271. https://doi.org/10.1002/wics.95
  27. ^ King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331(6018), 719-721.
  28. ^ Giles, J. (2012). Computational social science: Making the links. Nature, 488(7412), 448-450.
  29. ^ Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723
  30. ^ Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi. org/10.1007/978-1-4471-5661-1.
  31. ^ Ruppert, E., Law, J., Savage, M. (2013) Reassembling social science methods: The challenge of digital devices. Theory, Culture & Society 30(4): 22–46.
  32. ^ Rogers, R. (2019) Doing Digital Methods. North Tyneside: SAGE.
  33. ^ Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons.
  34. ^ https://ischool.umd.edu/centers-and-labs/soda
  35. ^ http://socialdatalab.net/
  36. ^ https://sodas.ku.dk.
  37. ^ 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.
  38. ^ Stopczynski A, Sekara V, Sapiezynski P, Cuttone A, Madsen MM, Larsen JE, et al. (2014) Measuring Large-Scale Social Networks with High Resolution. PLoS ONE 9(4)
  39. ^ Sekara V, Stopczynski A, Lehmann S (2016) Fundamental structures of dynamic social networks. Proceedings of the National Academy of Sciences 113(36): 9977–9982.
  40. ^ Sapiezynski, P., Stopczynski, A., Lassen, D.D. et al. Interaction data from the Copenhagen Networks Study. Sci Data 6, 315 (2019).
  41. ^ https://www.oii.ox.ac.uk/news-events/news/oxford-internet-institute-launches-masters-and-doctoral-programmes-in-social-data-science-applications-invited-from-sept-2017/
  42. ^ https://nyheder.ku.dk/alle_nyheder/2018/12/kandidat-i-social-datavidenskab/
  43. ^ https://studies.ku.dk/masters/social-data-science/
  44. ^ https://hbr.org/2009/05/the-social-data-revolution.html.
  45. ^ https://www.humanetech.com
  46. ^ https://www.penguinrandomhouse.com/books/598206/zucked-by-roger-mcnamee/
  47. ^ O’Neil , C. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Books
  48. ^ Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. ICWSM'14: Proceedings of the 8th International Conference on Weblogs and Social Media.
  49. ^ Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy in human behaviour in the age of information. Science, 347, 509-514
  50. ^ "UMD College of Information Studies, Social Data Science Center".
  51. ^ "University of Copenhagen, Copenhagen Center for Social Data Science".}
  52. ^ "OII's Social Data Science".
  53. ^ "University of Helsinki, Centre for Social Data Science".
Retrieved from "https://en.wikipedia.org/w/index.php?title=Social_data_science&oldid=1229570750"