Social science is one of the branches of science, devoted to the study of societies and the relationships among members within those societies. The term was formerly used to refer to the field of sociology, the original "science of society", established in the 18th century. In addition to sociology, it now encompasses a wide array of academic disciplines, including anthropology, archaeology, economics, geography, linguistics, management, communication studies, psychology, culturology and political science.
Computational archaeology is a subfield of digital archeology that focuses on the analysis and interpretation of archaeological data using advanced computational techniques. This field employs data modeling, statistical analysis, and computer simulations to understand and reconstruct past human behaviors and societal developments. By leveraging Geographic Information Systems (GIS), predictive modeling, and various simulation tools, computational archaeology enhances the ability to process complex archaeological datasets, providing deeper insights into historical contexts and cultural heritage.
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005), there are three perspectives of text mining: information extraction, data mining, and knowledge discovery in databases (KDD). Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational behavior, sociology, political science, economics, anthropology, geography, engineering, archaeology and linguistics.
Content analysis is the study of documents and communication artifacts, which might be texts of various formats, pictures, audio or video. Social scientists use content analysis to examine patterns in communication in a replicable and systematic manner. One of the key advantages of using content analysis to analyse social phenomena is their non-invasive nature, in contrast to simulating social experiences or collecting survey answers.
In sociology, social complexity is a conceptual framework used in the analysis of society. In the sciences, contemporary definitions of complexity are found in systems theory, wherein the phenomenon being studied has many parts and many possible arrangements of the parts; simultaneously, what is complex and what is simple are relative and change in time.
Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.
Macrosociology is a large-scale approach to sociology, emphasizing the analysis of social systems and populations at the structural level, often at a necessarily high level of theoretical abstraction. Though macrosociology does concern itself with individuals, families, and other constituent aspects of a society, it does so in relation to larger social system of which such elements are a part. The approach is also able to analyze generalized collectivities.
Quantitative history is a method of historical research that uses quantitative, statistical and computer resources. It is a type of the social science history and has four major journals: Historical Methods, Journal of Interdisciplinary History, the Social Science History, and Cliodynamics: The Journal of Quantitative History and Cultural Evolution.
Digital humanities (DH) is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities. It includes the systematic use of digital resources in the humanities, as well as the analysis of their application. DH can be defined as new ways of doing scholarship that involve collaborative, transdisciplinary, and computationally engaged research, teaching, and publishing. It brings digital tools and methods to the study of the humanities with the recognition that the printed word is no longer the main medium for knowledge production and distribution.
Historical sociology is an interdisciplinary field of research that combines sociological and historical methods to understand the past, how societies have developed over time, and the impact this has on the present. It emphasises a mutual line of inquiry of the past and present to understand how discrete historical events fit into wider societal progress and ongoing dilemmas through complementary comparative analysis.
Sociology is the scientific study of human society that focuses on society, human social behavior, patterns of social relationships, social interaction, and aspects of culture associated with everyday life. Regarded as a part of both the social sciences and humanities, sociology uses various methods of empirical investigation and critical analysis to develop a body of knowledge about social order and social change. Sociological subject matter ranges from micro-level analyses of individual interaction and agency to macro-level analyses of social systems and social structure. Applied sociological research may be applied directly to social policy and welfare, whereas theoretical approaches may focus on the understanding of social processes and phenomenological method.
Cliodynamics is a transdisciplinary area of research that integrates cultural evolution, economic history/cliometrics, macrosociology, the mathematical modeling of historical processes during the longue durée, and the construction and analysis of historical databases.
Geoffrey Nigel Gilbert is a British sociologist and a pioneer in the use of agent-based models in the social sciences. He is the founder and director of the Centre for Research in Social Simulation, author of several books on computational social science, social simulation and social research and past editor of the Journal of Artificial Societies and Social Simulation (JASSS), the leading journal in the field.
Kathleen M. Carley is an American computational social scientist specializing in dynamic network analysis. She is a professor in the School of Computer Science in the Carnegie Mellon Institute for Software Research at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the Heinz College, the Department of Engineering and Public Policy, and the Department of Social and Decision Sciences.
Culturomics is a form of computational lexicology that studies human behavior and cultural trends through the quantitative analysis of digitized texts. Researchers data mine large digital archives to investigate cultural phenomena reflected in language and word usage. The term is an American neologism first described in a 2010 Science article called Quantitative Analysis of Culture Using Millions of Digitized Books, co-authored by Harvard researchers Jean-Baptiste Michel and Erez Lieberman Aiden.
Historical dynamics broadly includes the scientific modeling of history. This might also be termed computer modeling of history, historical simulation, or simulation of history - allowing for an extensive range of techniques in simulation and estimation. Historical dynamics does not exist as a separate science, but there are individual efforts such as long range planning, population modeling, economic forecasting, demographics, global modeling, country modeling, regional planning, urban planning and many others in the general categories of computer modeling, planning, forecasting, and simulations.
The Seshat: Global History Databank is an international scientific research project of the nonprofit Evolution Institute. Founded in 2011, the Seshat: Global History Databank gathers data into a single, large database that can be used to test scientific hypotheses. The Databank consults directly with expert scholars to code what historical societies and their environments were like in the form of accessible datapoints and thus forms a digital storehouse for data on the political and social organization of all human groups from the early modern back to the ancient and neolithic periods. The organizers of this research project contend that the mass of data then can be used to test a variety of competing hypotheses about the rise and fall of large-scale societies around the globe which may help science provide answers to global problems.
The Evolution Institute (EI) is a non-profit organization whose mission is to apply science-based solutions and use evidence-based best practices to solve today’s most pressing social issues to improve quality of life.
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 or unstructured data. 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 cdomain knowledge and specialized theories from the social sciences with programming, statistical and other data analysis skills.
