Social data science: Difference between revisions
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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]]. |
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]]. |
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==Criticism and debates== |
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[[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: |
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* [[Surveillance capitalism]] |
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* Digital [[disinformation]] |
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* [[Algorithmic bias]] |
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* 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> |
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* [[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]] |
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* [[Data governance]] |
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==Impact and examples== |
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SDS research is typically published in multidisciplinary journals, including top general journals Science, Nature, and PNAS, as well as notable specialized journals such as: |
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Nature Human Behaviour |
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Nature Computational Science |
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The Journal of Computational Social Science |
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Big Data and Society |
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Science Advances |
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Nature Communications |
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Scientific Reports |
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PLOS ONE |
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In addition, SDS research is published in the top social science field journals including American Sociological Review, Psychological Science, American Economic Review, Current Anthropology |
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== References == |
== References == |
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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].
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 [36] [37]. 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
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 [38] [39]. 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
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 [40] [41] 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[42] and digital anthropology[43], 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
The origin of term “social data science” coincided with the emergence of a number of research centers and degree programs [44] [45] [46]. 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[47] [48] [49] [50] 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 [51] which was followed by the two-year master’s programme at the University of Copenhagen in 2020[52] [53]. Since then, an increasing number of universities have begun to offer graduate programs or specializations in social data science [link to Education and Research Institutions]
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[54]. 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
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 [55] [56] [57] 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:
- Surveillance capitalism
- Digital disinformation
- Algorithmic bias
- The replication and validity crisis on the social sciences [58] [59]
- Ethics[60] and privacy
- Data governance
Impact and examples
SDS 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, SDS research is published in the top social science field journals including American Sociological Review, Psychological Science, American Economic Review, Current Anthropology
References
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- ^ Venturini, T. & Munk, A.K. (2022). Controversy Mapping: A Field Guide. Cambridge: Polity Press
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- ^ Paff, S. (2021). Anthropology by Data Science. Annals of Anthropological Practice 46 (1): 7- 18.
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- ^ 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/
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- ^ https://www.humanetech.com
- ^ https://www.penguinrandomhouse.com/books/598206/zucked-by-roger-mcnamee/
- ^ O’Neil, C. (2016). Weapons of Math Destruction:How Big Data Increases Inequality and Threatens Democracy. Crown Books
- ^ Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls.
- ^ ICWSM'14: Proceedings of the 8th International Conference on Weblogs and Social Media.
- ^ Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy in human behaviour in the age of information. Science, 347, 509-514