📜 Post with us on Reddit
📝 Create a new article
🏭 Create a company page
🇬🇧 - in English
🇫🇷 - en Français (FR)
🇵🇹 - em Português (PT)
🇩🇪 - in Deutsch (DE)
🇯🇵 - 日本語で (JA)
Sponsored articles

You can edit almost every page by Creating an account. Otherwise, see the FAQ.

Social data science

From EverybodyWiki Bios & Wiki

Script error: No such module "AfC submission catcheck".


Definition[edit]

Social data science (SDS) is an interdisciplinary field that addresses social science problems by applying or designing computational and digital methods. As the name implies, SDS is located primarily within the social sciences, but it relies on technical advances in fields like data science, network science, and computer science (see Methods section). 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[edit]

Overview[edit]

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 digital methods [1] [2] [3] [4] [5]

Common SDS methods include:[6] [7] [8]

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 [22] [23] [24] [25] [26] [27]. For example by using mixed methods [28] to digitize qualitative data and analyzing it via computational methods, or by qualitatively analyzing and interpreting quantitative data [29] [30].

Data[edit]

Social data scientists use both digitized data [31] (e.g. old books that have been digitized) and natively digital data (e.g. social media posts) [32] [33]. 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[edit]

Social Sciences[edit]

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 [34]. 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 [35] [36]. 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 [37] [38] [39].

Data Science[edit]

SDS is a form of data science in that it applies advanced computational methods and statistics to gain information and insights from data [40] [41]. 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[edit]

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 [42] [43]. 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[edit]

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 [44] [45] 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[46] and digital anthropology[47], 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[edit]

The origin of term “social data science” coincided with the emergence of a number of research centers and degree programs [48] [49] [50]. 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 [51] [52] [53] [54] 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 [55] which was followed by the two-year master’s programme at the University of Copenhagen in 2020[56] [57]. Since then, an increasing number of universities have begun to offer graduate programs or specializations in social data science

SDS 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 (see Methods and Relations to other Fields). 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[58]. 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[edit]

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 [59] [60] [61] in the tech-lash that followed in its wake as result of, among other things, the Cambridge Analytica Scandal. SDS researchers and research projects have been especially impactful in debates and criticism revolving around:

Impact and examples[edit]

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


_____________________________________________________________________________________________________


Books: https://www.bitbybitbook.com/

http://www.bigdatasocialscience.com/

Pentland A (2015) Social Physics: How Social Networks Can Make Us Smarter. London: Penguin.

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.

Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press.

Matti Nelimarkka. 2022.Computational Thinking and Social Science Combining Programming, Methodologies and Fundamental Concepts. London: Sage


Research projects:

The Copenhagen Networks Study [link til History of the field]

The Atlas of Economic Complexity

National Internet Laboratory

HOPE - How Democracies Cope with Covid19: A Data-Driven Approach

DISTRACT: The Political Economy of Digital Attention in Denmark

BiasExplained: Pushing Algorithmic Fairness with Models and Experiments


Societal impact:

Next to the academic impact, important results and key societal debates and impacts are often picked up by media outlets as well as public and private organizations


Education and Research Institutions[edit]

There are multiple specific definitions of social data science, but several institutions around the world currently offer degree and research programs under the rubric of Social Data Science.

Education

M.Sc. in Social Data Science - University of Copenhagen

​MSc in Social Data Science - University of Oxford

MSc in Social and Economic Data Science (SEDS) - University of Konstanz

BSc in Social Data Science - University of Hong Kong

P.Grad.Dip in Social Data Science - University of Dublin

the London School of Economics, the Central European University, the University of Essex, the University of Aalborg, University College Dublin, Witten/Herdecke University, KU Leuven, and the University of Sussex.


Research

Copenhagen Center for Social Data Science (SODAS) - University of Copenhagen

Center for Social Data Science - University of Helsinki

Social Data Science Lab - Cardiff University

SoDa Laboratories - Monash University

Mannheim Center for Data Science - University of Mannheim

Social & Behavioral Data Science Centre (SoBe DSC) - University of Amsterdam

Social Data Science - Alan Turing Institute (London)

Social Data Science Center - University of Maryland

Centre for Social Data Analytics - Auckland University of Technology

MASSHINE – Aalborg University


Journals[edit]

Professions and industry[edit]

Social data scientists are in high demand [ref] across society, specifically for employers valuing interdisciplinary skills, and can be found working as:

Industry Researchers: Typical workplaces: governments, companies and corporations, independent research institutes, foundations, NGOs. Typical titles: researcher, data manager, data steward, data scientist, data engineer, consultant, manager, director, partner, politicians, data analyst, software developer, BI, UX, UI.Researcher Academic Researchers: Ph.D. Students, Researchers, Postdocs, Professors

Entrepreneurs: Start your own business using social data science methods to solve real-world social problems. Typical titles: CTO, CEO, Chief Data Scientist


Sub branches[edit]

Social data science is still a new field, with developing branches. Broadly speaking the field can be divided into a range of method-based sub-fields:


Method-based sub-fields

Network Science: Network analysis is often utilized to visualize or study network dynamics in social data science studies. This includes for instance social media networks.

Mixed Digital Methods: In computer-assisted qualitative analysis, the researcher often utilizes computational methods such as natural language processing techniques or topic modelling to explore a corpus of text, such as parliamentary speeches or Twitter data.

Machine Learning for Causal Inference: The social sciences are often interested in finding causal relationships between variables. This is of special interest to social data science, where multiple fields of research try to establish appropriate policy responses to contemporary societal issues. Often, drawing from research from Judea Pearls directed acyclical graph approach and the Neymann-Rubin Causal model to inform whether there exists a causal relationship between two (or more) variables. Furthermore, incorporating machine learning into causal inference is of great interest.

Natural Language Processing: Applied natural language processing is the adaptation and repurposing of methods from natural language processing and the application of these methods to questions of social behavior.

Geospatial Social Data Analysis:


Themes


Algorithmic Bias and Fairness: Considering how algorithms play a still larger role in humans everyday life, the study of fairness in this context has grown as a field. Especially whether miniorieties are negatively or positively impacted by these algorithms.

Polarization and Misinformation: Many scholars use enormous amounts of granular data generated by social media and political agents to study social contagion, the spread of misinformation and disinformation. These studies often use text or social media interactions to explore how politicians and/or the public behave and interact in the digital and physical arena.

Machine Behavior: (Add Ingo)

Climate Social Data Science: The intersection between climate science, and digital (behavioral) data. This includes climate activism on social media and using digital trace data to investigate how people and societies are impacted by rising temperatures (CITE: Rising Temperature Erode Human Sleep Globally].

Attention Economy: In later years, the attention economy has become a field of study related to the concerns brought about by digitalization of society that capabilities of sustained attention are suffering from constant connectivity.


References[edit]

  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. ↑ Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press
  5. ↑ Veltri, G.A. (2019). Digital social research. Polity Press.
  6. ↑ 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.
  7. ↑ Veltri, G. 2020. Digital Social Research. Polity Press.
  8. ↑ Salganik, M. (2018). Bit by Bit: Social Research in the Digital Age. Princeton UP.
  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. ↑ Paff, S. (2021). Anthropology by Data Science. Annals of Anthropological Practice 46 (1): 7- 18.
  19. ↑ 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.
  20. ↑ Munk, A.K., Olesen, A.G., & Jacomy, M. (2022). The Thick Machine: Anthropological AI between explanation and explication. Big Data & Society, 9(1).
  21. ↑ Pedersen, M.A. (Eds.) (2023). Machine Anthropology. Special issue of Big Data & Society, 10(1).
  22. ↑ Nelson, L.K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research 49(1): 3-42.
  23. ↑ 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.
  24. ↑ Carlsen, H.B. & Ralund, S. (2022). Computational Grounded Theory Revisited. Big Data and Society 9 (1).
  25. ↑ Mills, K.A. (2019) Big Data for Qualitative Research. New York: Taylor & Francis.
  26. ↑ Jemielniak, D. (2020). Thick big data — doing digital social science. Oxford University Press
  27. ↑ Grigoropoulou, N. and Small, M.L. (2022). The data revolution in social science needs qualitative research. Nature Human Behaviour 6: 904–906.
  28. ↑ 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
  29. ↑ Brandt, P. & Timmermans, S. (2021). Abductive Logic of Inquiry for Quantitative Researchin the Digital Age. Sociological Science 8: 191-210.
  30. ↑ Blok, A., Bornakke, T., Carlsen, H.B., Madsen, M.M., Ralund, S. & Pedersen, M.A. (2017). Stitching together the heterogeneous party: a complementary social data science experiment. Big Data & Society 4 (2). https://journals.sagepub.com/doi/10.1177/2053951717736337
  31. ↑ 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.]
  32. ↑ 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.]
  33. ↑ https://www.nature.com/collections/cadaddgige
  34. ↑ Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons.
  35. ↑ Salganik, M.J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  36. ↑ Veltri, G. A. (2019). Digital social research. Polity Press.
  37. ↑ Lazer, D., Hargittai, E., Freelon, D., et al. (2021). Meaningful measures of human society in the twenty-first century. Nature 595(7866): 189–196.
  38. ↑ Rahwan, I., Cebrian, M., Obradovich, N. et al. (2019). Machine behaviour. Nature 568, 477–486. https://doi.org/10.1038/s41586-019-1138-y
  39. ↑ Pedersen, M.A. (Eds.) (2023). Machine Anthropology. Special issue of Big Data & Society, 10(1).
  40. ↑ King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331(6018), 719-721.
  41. ↑ Giles, J. (2012). Computational social science: Making the links. Nature, 488(7412), 448-450.
  42. ↑ Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723
  43. ↑ Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1.
  44. ↑ Ruppert, E., Law, J., Savage, M. (2013). Reassembling social science methods: The challenge of digital devices. Theory, Culture & Society 30(4): 22–46.
  45. ↑ Rogers, R. (2019). Doing Digital Methods. North Tyneside: SAGE.
  46. ↑ Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons.
  47. ↑ Varis, P. (2015). Digital ethnography. In The Routledge handbook of language and digital communication (pp. 55-68). Routledge.
  48. ↑ https://ischool.umd.edu/centers-and-labs/soda
  49. ↑ https://sodas.ku.dk
  50. ↑ http://socialdatalab.net/
  51. ↑ Blok, A. & Pedersen, M.A. (2014). Complementary social science? Quali-quantitative experiments in a Big Data world. Big Data & Society 1(2): 1–6.
  52. ↑ Stopczynski, A., Sekara, V., Sapiezynski, P., Cuttone, A., Madsen, M.M., Larsen, J.E., et al. (2014). Measuring Large-Scale Social Networks with High Resolution. PLoS ONE 9(4): e95978. https://doi.org/10.1371/journal.pone.0095978
  53. ↑ Sekara, V., Stopczynski, A., Lehmann, S. (2016). Fundamental structures of dynamic social networks. Proceedings of the National Academy of Sciences 113(36): 9977–9982.
  54. ↑ Sapiezynski, P., Stopczynski, A., Lassen, D.D. et al. (2019). Interaction data from the Copenhagen Networks Study. Sci Data 6, 315. https://doi.org/10.1038/s41597-019-0325-x
  55. ↑ 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/
  56. ↑ https://nyheder.ku.dk/alle_nyheder/2018/12/kandidat-i-social-datavidenskab/
  57. ↑ https://studies.ku.dk/masters/social-data-science/
  58. ↑ Weigend, A. “The Social Data Revolution(s)”. https://hbr.org/2009/05/the-social-data-revolution
  59. ↑ https://www.humanetech.com
  60. ↑ https://www.penguinrandomhouse.com/books/598206/zucked-by-roger-mcnamee/
  61. ↑ O’Neil, C. (2016). Weapons of Math Destruction:How Big Data Increases Inequality and Threatens Democracy. Crown Books
  62. ↑ Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls.
  63. ↑ ICWSM'14: Proceedings of the 8th International Conference on Weblogs and Social Media.
  64. ↑ Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy in human behaviour in the age of information. Science, 347, 509-514


This article "Social data science" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Social data science. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.


Retrieved from "https://en.everybodywiki.com/index.php?title=Social_data_science&oldid=3887595"
License CC BY-SA 3.0
Powered by MediaWiki
Powered by Semantic MediaWiki