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== Definitions ==


Social data science is an interdisciplinary field that addresses [[social science]] problems by applying or designing computational and digital methods.
==Definition==
It is located primarily within the [[social sciences|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. [[Survey (human research)|survey]]s) or unstructured data (e.g. [[Digital footprint|digital footprints]]).
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 [[Social data science#Methods|Methods section]]). 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 [[Computer programming|programming]], statistical and other [[data analysis]] skills.
A social data scientist combines concepts and specialized theories from the social sciences with [[Computer programming|programming]], statistical and other [[data analysis]] skills.


==Methods==
==Methods==


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]], [[data science]] and [[network science]].
===Overview===
In addition, and interdisciplinary data science fields such as [[natural language processing]] (NLP) and .
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 digital 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>Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press </ref> <ref>Veltri, G.A. (2019). Digital social research. Polity Press.</ref>
Sometimes it also works on [[Qualitative research|qualitative data]], such as interviews, through [[natural language processing]].

Methods include
'''Common SDS methods include:'''<ref>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.</ref> <ref>Veltri, G. 2020. Digital Social Research. Polity Press.</ref> <ref>Salganik, M. (2018). Bit by Bit: Social Research in the Digital Age. Princeton UP.</ref>


''Quantitative methods:''
* [[Machine learning]]
* [[Machine learning]]
* [[Deep learning]]
* [[Social network analysis]]
* [[Social network analysis]]
* [[Randomized controlled trials]]
* [[Randomized controlled trials]]
* [[Natural language processing]] (NLP), especially through ''text as data''<ref>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.</ref>
* [[Natural language processing]] (NLP)
* [[Controversy mapping]] <ref>Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press</ref>
* [[Survey (human research)| Surveys]]

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

''Mixed digital methods:''
* [[Spatial analysis]]
* [[Spatial analysis]]
* [[Controversy mapping]] <ref>Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press</ref>
* [[Mixed methods]]
* [[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>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. (Eds.) (2023). Machine Anthropology. Special issue of Big Data & Society, 10(1).</ref>


In addition, social data scientists have sought to introduce computational methods to replicate existing social science method with their computational counterparts, such as
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>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> <ref>Mills, K.A. (2019) Big Data for Qualitative Research. New York: Taylor & Francis.</ref> <ref>Jemielniak, D. (2020). Thick big data — doing digital social science. Oxford University Press</ref> <ref>Grigoropoulou, N. and Small, M.L. (2022). The data revolution in social science needs qualitative research. Nature Human Behaviour 6: 904–906.</ref>. For example by using [[mixed methods]] <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> to digitize qualitative data and analyzing it via computational methods, or by qualitatively analyzing and interpreting [[quantitative data]] <ref>Brandt, P. & Timmermans, S. (2021). Abductive Logic of Inquiry for Quantitative Researchin the Digital Age. Sociological Science 8: 191-210.</ref> <ref>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</ref>.
* [[grounded theory]] via computational grounded theory<ref>Carlsen, H.B. & Ralund, S. (2022). Computational Grounded Theory Revisited. Big Data and Society 9 (1).</ref><ref>Nelson, L. K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research, 49(1), 3–42. https://doi.org/10.1177/0049124117729703</ref>


Sometimes social data science takes place in a [[Multimethodology|mixed methods settings]].<ref>Ford, H. (2014) Big data and small: Collaborations between ethnographers and data scientists. Big Data & Society 1(2): 205395171454433.</ref>
===Data===


SDS is closely related to [[Computational Social Science]], but also sometimes includes [[qualitative research]] and [[mixed digital methods]] <ref>Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723</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>Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press </ref> <ref>Veltri, G.A. (2019). Digital social research. Polity Press.</ref>
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>{{cite web | url=https://www.nature.com/collections/cadaddgige | title=Computational social science | date=7 July 2021 }}</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.


=== Data ===
'''Sources of SDS data include:'''
* Text data
* Sensor data
* Register data
* Survey data
* Geo-location data
* Observational data


Social data scientists use both data specially collected for research purposes and data appropriated for research, or as Salganic<ref>Salganik, M.J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.</ref> calls them, ''custommade'' and ''readymade'' data.
==Relations to other fields==
Sometimes, the latter is also refered 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.


===Social Sciences===
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. Polity Press.</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>.


== Relations to other fields ==
===Data Science===
SDS is a form of [[data science]] in that it applies advanced [[computational methods]] and [[statistics]] to gain information and insights from data <ref>King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331(6018), 719-721.</ref> <ref>Giles, J. (2012). Computational social science: Making the links. Nature, 488(7412), 448-450.</ref>. 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===
===Social sciences===
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 scientist]]s to better fit the questions and data of the social sciences as well as their specialized domain knowledge and theories <ref>Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723</ref> <ref>Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1.</ref>. 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.


Social data science 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]].
===Digital Methods===
Social data also differs from traditional social science in two ways:
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 <ref>Ruppert, E., Law, J., Savage, M. (2013). Reassembling social science methods: The challenge of digital devices. Theory, Culture & Society 30(4): 22–46.</ref> <ref>Rogers, R. (2019). Doing Digital Methods. North Tyneside: SAGE.</ref> 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]]<ref>Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons.</ref> and [[digital anthropology]]<ref>Varis, P. (2015). Digital ethnography. In The Routledge handbook of language and digital communication (pp. 55-68). Routledge.</ref>, but to a higher degree aspires to augment [[qualitative data]] and [[digital methods]] with state of the art [[data science]] techniques.


# 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. Polity Press.</ref>.
==History of the field==
# 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<ref>Cioffi-Revilla, C. (2010). Computational social science. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 259–271. https://doi.org/10.1002/wics.95</ref>


===Data Science===
The origin of term “social data science” coincided with the emergence of a number of research centers and degree programs <ref>{{cite web | url=https://ischool.umd.edu/centers-and-labs/soda | title=Social Data Science Center (SoDa) }}</ref> <ref>https://sodas.ku.dk</ref> <ref>http://socialdatalab.net/</ref>. 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 science|social]] and [[computational science]]s was rooted in the Copenhagen Networks Study <ref>Blok, A. & Pedersen, M.A. (2014). Complementary social science? Quali-quantitative experiments in a Big Data world. Big Data & Society 1(2): 1–6.</ref> <ref>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</ref> <ref>Sekara, V., Stopczynski, A., Lehmann, S. (2016). Fundamental structures of dynamic social networks. Proceedings of the National Academy of Sciences 113(36): 9977–9982.</ref> <ref>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</ref> 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 <ref>{{cite web | url=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/ | title=OII &#124; Oxford Internet Institute Launches Master's and Doctoral Programmes in Social Data Science: Applications Invited from Sept 2017 }}</ref> which was followed by the two-year master’s programme at the University of Copenhagen in 2020<ref>{{cite web | url=https://nyheder.ku.dk/alle_nyheder/2018/12/kandidat-i-social-datavidenskab/ | title=Nyheder - Forskning - Videnskab | date=21 July 2009 }}</ref> <ref>{{cite web | url=https://studies.ku.dk/masters/social-data-science/ | title=Master of Science (MSC) in Social Data Science | date=4 February 2013 }}</ref>. Since then, an increasing number of universities have begun to offer [[Social data science#Education and Research Institutions|graduate programs or specializations in social 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 <ref>King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331(6018), 719-721.</ref> <ref>Giles, J. (2012). Computational social science: Making the links. Nature, 488(7412), 448-450.</ref>.
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 [[Social data science#Methods|Methods]] and [[Social data science#Relations to other Fields|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]]<ref>Weigend, A. “The Social Data Revolution(s)”. https://hbr.org/2009/05/the-social-data-revolution</ref>. 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]].
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.

==Criticism and debates==

[[Data scientist]]s 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 <ref>https://www.humanetech.com</ref> <ref>https://www.penguinrandomhouse.com/books/598206/zucked-by-roger-mcnamee/</ref> <ref>O’Neil, C. (2016). Weapons of Math Destruction:How Big Data Increases Inequality and Threatens Democracy. Crown Books</ref> in the tech-lash that followed in its wake as result of, among other things, the [[Facebook–Cambridge Analytica data scandal|Cambridge Analytica Scandal]]. SDS researchers and research projects have been especially impactful in debates and criticism revolving around:

* [[Surveillance capitalism]]
* Digital [[disinformation]]
* [[Algorithmic bias]]
* The replication and validity crisis on the social sciences <ref>Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls.</ref> <ref>ICWSM'14: Proceedings of the 8th International Conference on Weblogs and Social Media.</ref>
* [[Ethics]]<ref>Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy in human behaviour in the age of information. Science, 347, 509-514</ref> and [[privacy]]
* [[Data governance]]


==Impact and examples==
==Impact and examples==


SDS research is typically published in multidisciplinary journals, including top general journals [[Science (journal)|Science]], [[Nature (journal)|Nature]], and [[Proceedings of the National Academy of Sciences of the United States of America|PNAS]], as well as notable specialized journals such as:
Social data science research is typically published in multidisciplinary journals, including top general journals [[Science (journal)|Science]], [[Nature (journal)|Nature]], and [[Proceedings of the National Academy of Sciences of the United States of America|PNAS]], as well as notable specialized journals such as:


* [[Nature Human Behaviour]]
* [[Nature Human Behaviour]]
Line 97: Line 67:
* [[PLOS ONE]]
* [[PLOS ONE]]


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


_____________________________________________________________________________________________________


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==

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==



==Professions and industry==

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==

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.


== Institutional status ==
Machine Behavior: (Add Ingo)


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


* [[University of Maryland]], Social Data Science Center <ref>{{cite web | url=https://ischool.umd.edu/centers-and-labs/soda | title=UMD College of Information Studies, Social Data Science Center}}</ref>
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.
* [[University of Copenhagen]], Centre for Social Data Science<ref>{{cite web | url=https://sodas.ku.dk/ | title =University of Copenhagen, Copenhagen Center for Social Data Science}}}</ref>
* [[University of Oxford]], [[Oxford Internet Institute]]<ref>{{cite web | url=https://www.oii.ox.ac.uk/research/social-data-science/ | title= OII's Social Data Science}}</ref>
* [[University of Helsinki]], Centre for Social Data Science<ref>{{cite web | url=https://www.helsinki.fi/en/networks/centre-social-data-science | title=University of Helsinki, Centre for Social Data Science}}</ref>




Latest revision as of 17:47, 12 March 2024


Definitions[edit]

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[edit]

Social data science employs a wide range of quantitative - both established methods in social science as well as new methods developed in computer science, data science and network science. In addition, and interdisciplinary data science fields such as natural language processing (NLP) and . Sometimes it also works on qualitative data, such as interviews, through natural language processing. Methods include

In addition, social data scientists have sought to introduce computational methods to replicate existing social science method with their computational counterparts, such as

Sometimes social data science takes place in a mixed methods settings.[5]

SDS is closely related to Computational Social Science, but also sometimes includes qualitative research and mixed digital methods [6] [7] [8] [9]

Data[edit]

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 refered 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[edit]

Social sciences[edit]

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:

  1. 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].
  2. 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[edit]

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[edit]

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[edit]

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


References[edit]

  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. ^ Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press
  3. ^ Carlsen, H.B. & Ralund, S. (2022). Computational Grounded Theory Revisited. Big Data and Society 9 (1).
  4. ^ Nelson, L. K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research, 49(1), 3–42. https://doi.org/10.1177/0049124117729703
  5. ^ Ford, H. (2014) Big data and small: Collaborations between ethnographers and data scientists. Big Data & Society 1(2): 205395171454433.
  6. ^ Lazer, D., et al. (2009). Computational Social Science. Science, 323(5915), 721-723
  7. ^ Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer London. https://doi.org/10.1007/978-1-4471-5661-1.
  8. ^ Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press
  9. ^ Veltri, G.A. (2019). Digital social research. Polity Press.
  10. ^ Salganik, M.J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  11. ^ Salganik, M.J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  12. ^ Veltri, G. A. (2019). Digital social research. Polity Press.
  13. ^ Cioffi-Revilla, C. (2010). Computational social science. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 259–271. https://doi.org/10.1002/wics.95
  14. ^ King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331(6018), 719-721.
  15. ^ Giles, J. (2012). Computational social science: Making the links. Nature, 488(7412), 448-450.
  16. ^ "UMD College of Information Studies, Social Data Science Center".
  17. ^ "University of Copenhagen, Copenhagen Center for Social Data Science".}
  18. ^ "OII's Social Data Science".
  19. ^ "University of Helsinki, Centre for Social Data Science".
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