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==Definition==
==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. [[Survey (human research)|survey]]s) 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.
Social data science (SDS) is an interdisciplinary field that addresses [[social science]] problems by [[Social data Science#Methods|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. [[Survey (human research)|survey]]s) 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==
==Methods==


===Overview===
===Overview===
SDS employs a wide range of [[Quantitative research|quantitative]] and [[Qualitative research|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]] <ref>Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723</ref> <ref>Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.</ref> <ref>Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi. org/10.1007/978-1-4471-5661-1. </ref> <ref>Pentland A (2015) Social Physics: How Social Networks Can Make Us Smarter. London: Penguin. </ref> <ref>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. </ref> <ref>Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press </ref> <ref>Veltri, G. A. (2019). Digital social research. John Wiley & </ref> <ref>Matti Nelimarkka. 2022. Computational Thinking and Social Science: Combining Programming, Methodologies and Fundamental Concepts. London: Sage </ref>
SDS employs a wide range of [[Quantitative research|quantitative]] and [[Qualitative research|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]] <ref>Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723</ref> <ref>Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.</ref> <ref>Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1. </ref> <ref>Pentland, A. (2015) Social Physics: How Social Networks Can Make Us Smarter. London: Penguin. </ref> <ref>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. </ref> <ref>Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press </ref> <ref>Veltri, G. A. (2019). Digital social research.</ref> <ref>Nelimarkka, M. 2022. Computational Thinking and Social Science: Combining Programming, Methodologies and Fundamental Concepts. London: Sage </ref>


'''Common SDS methods include:'''
'''Common SDS methods include:'''
Line 15: Line 15:
''Qualitative methods:''
''Qualitative methods:''
* [[Interviewing]]
* [[Interviewing]]
* Observation <ref>Watching Closely: A Guide to Ethnographic Observation. Christena Nippert-Eng. Oxford University Press. 2015.</ref>
* Observation <ref> Nippert-Eng, C. (2015). Watching Closely: A Guide to Ethnographic Observation. Oxford University Press.</ref>
* [[Ethnography]]
* [[Ethnography]]
* [[Content analysis]]
* [[Content analysis]]
Line 31: Line 31:


* [[Spatial analysis]]
* [[Spatial analysis]]
* [[Controversy mapping]] <ref>Tommaso Venturini and Anders Kristian Munk. 2022. Controversy Mapping: A Field Guide. Cambridge: Polity Press</ref>
* [[Controversy mapping]] <ref>Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press</ref>
* [[Mixed methods]]
* [[Mixed methods]]
* [[Quali-quantitative methods]] <ref>Ford H (2014) Big data and small: Collaborations between ethno- graphers and data scientists. Big Data & Society 1(2): 205395171454433.</ref> <ref>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.</ref> <ref>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</ref> <ref>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): 205395171877213.</ref> <ref>Isfeldt AS, Enggaard TR, Blok A, Pedersen MA. 2022. Grøn Genstart: A quali-quantitative micro-history of a political idea in real-time. Big Data & Society 9 (1)</ref>
* [[Quali-quantitative methods]] <ref>Ford, H. (2014) Big data and small: Collaborations between ethnographers and data scientists. Big Data & Society 1(2): 205395171454433.</ref> <ref>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.</ref> <ref>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</ref> <ref>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.</ref> <ref>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)</ref>
* [[Computational ethnography]] <ref>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.</ref> <ref>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</ref> <ref>Breslin, S, et al. 2022. Affective Publics: Performing Trust on Danish Twitter during the COVID-19 Lockdown”. Current Anthropology 63(2).</ref>
* [[Computational ethnography]] <ref>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.</ref> <ref>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</ref> <ref>Breslin, S., et al. 2022. Affective Publics: Performing Trust on Danish Twitter during the COVID-19 Lockdown”. Current Anthropology 63(2).</ref>
* [[Machine anthropology]] <ref>Paff, S. 2021. Anthropology by Data Science. Annals of Anthropological Practice 46 (1): 7- 18.</ref> <ref>Santucci J-F, Doja A and 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.</ref> <ref>Munk, A. K., Olesen, A. G., & Jacomy, M. (2022). The Thick Machine: Anthropological AI between explanation and explication. Big Data & Society, 9(1).</ref> <ref>Pedersen, M. A. (2023). Editorial introduction: Towards a machinic anthropology. Big Data & Society, 10(1).</ref>
* [[Machine anthropology]] <ref>Paff, S. (2021). Anthropology by Data Science. Annals of Anthropological Practice 46 (1): 7- 18.</ref> <ref>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.</ref> <ref>Munk, A.K., Olesen, A.G., & Jacomy, M. (2022). The Thick Machine: Anthropological AI between explanation and explication. Big Data & Society, 9(1).</ref> <ref>Pedersen, M.A. (2023). Editorial introduction: Towards a machinic anthropology. Big Data & Society, 10(1).</ref>


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 <ref>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</ref> <ref>Nelson LK. 2020. Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research 49(1): 3-42.</ref> <ref>Nelson LK, 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.</ref> <ref>Carlsen, Hjalmar Bang and Ralund, Snorre. 2022. Computational Grounded Theory Revisited. Big Data and Society 9 (1).</ref> . 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]].
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 <ref>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</ref> <ref>Nelson, L.K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research 49(1): 3-42.</ref> <ref>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.</ref> <ref>Carlsen, H.B. & Ralund, S. (2022). Computational Grounded Theory Revisited. Big Data and Society 9 (1).</ref> . 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===
===Data===


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.




Revision as of 14:36, 17 August 2023


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.


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.
  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
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