Social data science is an interdisciplinary field that addresses social science problems by applying or designing computational and digital methods. As the name implies, Social Data Science is located primarily within the social science, but it relies on technical advances in fields like data science, network science, and computer science. The data in Social Data Science is always about human beings and derives from social phenomena, and it could be structured data (e.g. surveys) or unstructured data (e.g. digital footprints). The goal of Social Data Science 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 Social data science employs a wide range of quantitative - 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. Social Data Science is closely related to Computational Social Science, but also sometimes includes qualitative data, and mixed digital methods. Common social data science methods include: Quantitative methods:
Machine learning Deep learning Social network analysis Randomized controlled trials Natural language processing (NLP), especially through text as data. surveys Qualitative methods:
Interviewing Observation Ethnography Content analysis Discourse analysis Mixed digital methods:
Controversy mapping Spatial analysis Quali-quantitative methods Computational ethnography 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. For example, by using mixed 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 (e.g. old books that have been digitized) and natively digital data (e.g. social media posts). 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 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. 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. SDS also differs from traditional social science in two ways.
First, its primary object 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. Secondly, more than simply applying existing quantitative and qualitative 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
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. Social data science 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, social data science mainly concerns the scientific study of digital social data and/or digital footprints from human behavior.
Computational Social Science Like computational social science, social data science 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. Unlike computational social science, social data science 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 social data science researchers are closely 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 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 and digital anthropology, but to a higher degree aspires to augment qualitative data and digital methods with state of the art data science techniques.
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