Social data science
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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
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