Social data science: Difference between revisions
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==Methods== |
==Methods== |
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Social data science employs a wide range of [[Quantitative research|quantitative]] - both established methods in [[social science]] as well as new methods developed in [[computer science]] |
Social data science employs a wide range of [[Quantitative research|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]]. |
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Social data science is closely related to [[Computational Social Science]], but also sometimes includes [[Qualitative research|qualitative data]], and mixed digital methods. |
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In addition, and interdisciplinary data science fields such as [[natural language processing]] (NLP) and . |
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Sometimes it also works on [[Qualitative research|qualitative data]], such as interviews, through [[natural language processing]]. |
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Common social data science methods include: |
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Methods include |
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* [[Machine learning]] |
* [[Machine learning]] |
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Revision as of 11:19, 17 June 2024
Social data science is an interdisciplinary field that addresses social science problems by applying or designing computational and digital methods. It is located primarily within the social science, but it relies on technical advances in fields like data science, network science, and computer science. Social data scientists work on data on human beings and derives from social phenomena, and it could be structured data (e.g. surveys) or unstructured data (e.g. digital footprints). A social data scientist combines concepts 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:
- Machine learning
- Social network analysis
- Randomized controlled trials
- Natural language processing (NLP), especially through text as data.[1]
- Controversy mapping[2]
- Spatial analysis
In addition, social data scientists have sought to introduce computational methods to replicate existing social science method with their computational counterparts, such as
- grounded theory via computational grounded theory[3][4]
Sometimes social data science takes place in a mixed methods settings.[5]
Social Data Science is closely related to Computational Social Science, but also sometimes includes qualitative research and mixed digital methods[6] [7] [8] [9]
Data
Social data scientists use both data specially collected for research purposes and data appropriated for research, or as Salganic[10] calls them, custommade and readymade data. Sometimes, the latter is also referred to found data, that is, 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.
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. Social data also differs from traditional social science in two ways:
- 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 [11] [12].
- beyond using traditional 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[13]
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 [14] [15] Unlike the broader field of data science, which involves any application and study involving the combination of computational and statistical methods, social data mainly concerns the scientific study of human behavior in groups or society.
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:
- Nature Human Behaviour
- Nature Computational Science
- The Journal of Computational Social Science
- Big Data and Society
- Science Advances
- Nature Communications
- Scientific Reports
- PLOS ONE
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
- University of Maryland, Social Data Science Center[16]
- University of Copenhagen, Centre for Social Data Science[17]
- University of Oxford, Oxford Internet Institute[18]
- University of Helsinki, Centre for Social Data Science[19]
References
- ^ 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.
- ^ Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press
- ^ Carlsen, H.B. & Ralund, S. (2022). Computational Grounded Theory Revisited. Big Data and Society 9 (1).
- ^ Nelson, L. K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research, 49(1), 3–42. https://doi.org/10.1177/0049124117729703
- ^ Ford, H. (2014) Big data and small: Collaborations between ethnographers and data scientists. Big Data & Society 1(2): 205395171454433.
- ^ Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723
- ^ Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1.
- ^ Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press
- ^ Veltri, G.A. (2019). Digital social research. Polity Press.
- ^ Salganik, M.J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
- ^ Salganik, M.J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
- ^ Veltri, G. A. (2019). Digital social research. Polity Press.
- ^ Cioffi-Revilla, C. (2010). Computational social science. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 259–271. https://doi.org/10.1002/wics.95
- ^ King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331(6018), 719-721.
- ^ Giles, J. (2012). Computational social science: Making the links. Nature, 488(7412), 448-450.
- ^ "UMD College of Information Studies, Social Data Science Center".
- ^ "University of Copenhagen, Copenhagen Center for Social Data Science".}
- ^ "OII's Social Data Science".
- ^ "University of Helsinki, Centre for Social Data Science".
 | This article needs additional or more specific categories. (June 2024) |