Social data science: Difference between revisions
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Social data scientists use both digitized data <ref>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.]</ref> (e.g. old books that have been digitized) and natively [[digital data]] (e.g. social media posts) <ref>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.]</ref> <ref>https://www.nature.com/collections/cadaddgige</ref>. 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. |
Social data scientists use both digitized data <ref>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.]</ref> (e.g. old books that have been digitized) and natively [[digital data]] (e.g. social media posts) <ref>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.]</ref> <ref>https://www.nature.com/collections/cadaddgige</ref>. 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. |
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Sources of SDS data include: |
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* Text data |
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* Sensor data |
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* Register data |
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* Survey data |
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* Geo-location data |
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* Observational data |
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==Relations to other fields== |
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===Social Sciences=== |
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SDS is part of the [[social science]]s 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 <ref>Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons.</ref>. 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 <ref>Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.</ref> <ref>Veltri, G. A. (2019). Digital social research.</ref>. 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 <ref>Lazer D, Hargittai E, Freelon D, et al. (2021) Meaningful measures of human society in the twenty-first century. Nature 595(7866): 189–196. </ref> <ref>Rahwan, I., Cebrian, M., Obradovich, N. et al. (2019) Machine behaviour. Nature 568, 477–486. https://doi.org/10.1038/s41586-019-1138-y</ref> <ref>Pedersen, M. A. Eds. (2023). Machine Anthropology. Special issue of Big Data & Society, 10(1).</ref>. |
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Revision as of 12:12, 21 August 2023
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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:
- Interviewing
- Observation [9]
- Ethnography
- Content analysis
- Discourse analysis
Quantitative methods:
- Machine learning
- Deep learning
- Social network analysis
- Randomized controlled trials/quasi randomized trials
- Natural language processing (NLP)
- Surveys
Mixed methods:
- Spatial analysis
- Controversy mapping [10]
- Mixed methods
- Quali-quantitative methods [11] [12] [13] [14] [15]
- Computational ethnography [16] [17] [18]
- Machine anthropology [19] [20] [21] [22]
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].
References
- ^ Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723
- ^ Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
- ^ Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1.
- ^ 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
- ^ Veltri, G. A. (2019). Digital social research.
- ^ Nelimarkka, M. (2022). Computational Thinking and Social Science: Combining Programming, Methodologies and Fundamental Concepts. London: Sage
- ^ Nippert-Eng, C. (2015). Watching Closely: A Guide to Ethnographic Observation. Oxford University Press.
- ^ Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press
- ^ Ford, H. (2014) Big data and small: Collaborations between ethnographers and data scientists. Big Data & Society 1(2): 205395171454433.
- ^ 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.
- ^ 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
- ^ 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.
- ^ 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)
- ^ 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.
- ^ 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
- ^ Breslin, S., et al. 2022. Affective Publics: Performing Trust on Danish Twitter during the COVID-19 Lockdown”. Current Anthropology 63(2).
- ^ Paff, S. (2021). Anthropology by Data Science. Annals of Anthropological Practice 46 (1): 7- 18.
- ^ 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.
- ^ Munk, A.K., Olesen, A.G., & Jacomy, M. (2022). The Thick Machine: Anthropological AI between explanation and explication. Big Data & Society, 9(1).
- ^ Pedersen, M.A. (2023). Editorial introduction: Towards a machinic anthropology. Big Data & Society, 10(1).
- ^ 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
- ^ Nelson, L.K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research 49(1): 3-42.
- ^ 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.
- ^ Carlsen, H.B. & Ralund, S. (2022). Computational Grounded Theory Revisited. Big Data and Society 9 (1).
- ^ 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.]
- ^ 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.]
- ^ https://www.nature.com/collections/cadaddgige
- ^ Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons.
- ^ Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
- ^ Veltri, G. A. (2019). Digital social research.
- ^ Lazer D, Hargittai E, Freelon D, et al. (2021) Meaningful measures of human society in the twenty-first century. Nature 595(7866): 189–196.
- ^ Rahwan, I., Cebrian, M., Obradovich, N. et al. (2019) Machine behaviour. Nature 568, 477–486. https://doi.org/10.1038/s41586-019-1138-y
- ^ Pedersen, M. A. Eds. (2023). Machine Anthropology. Special issue of Big Data & Society, 10(1).