Computational social science refers to the academic sub-disciplines concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. Fields include computational economics, computational sociology, cliodynamics and the automated analysis of contents, in social and traditional media. It focuses on investigating social and behavioral relationships and interactions through social simulation, modeling, network analysis, and media analysis.
Computational social science revolutionizes both fundamental legs of the scientific method: empirical research, especially through big data, by analyzing the digital footprint left behind through social online activities; and scientific theory, especially through computer simulation model building through social simulation. It is a multi-disciplinary and integrated approach to social survey focusing on information processing by means of advanced information technology. The computational tasks include the analysis of social networks, social geographic systems, social media content and traditional media content.
Computational social science work increasingly relies on the greater availability of large databases, currently constructed and maintained by a number of interdisciplinary projects, including:
The Seshat: Global History Databank, which systematically collects state-of-the-art accounts of the political and social organization of human groups and how societies have evolved through time into an authoritative databank. Seshat is affiliated also with the Evolution Institute, a non-profit think-tank that "uses evolutionary science to solve real-world problems."
D-PLACE: the Database of Places, Languages, Culture and Environment, which provides data on over 1,400 human social formations
Clio-Infra a database of measures of economic performance and other aspects of societal well-being on a global sample of societies from 1800 CE to the present
The analysis of vast quantities of historical newspaper content has been pioneered in, while other studies on similar data showed how periodic structures can be automatically discovered in historical newspapers. A similar analysis was performed on social media, again revealing strongly periodic structures.
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^Peter N. Peregrine, Atlas of Cultural Evolution, World Cultures 14(1), 2003
^Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts F Dzogang, T Lansdall-Welfare, N Cristianini - 2016 IEEE International Conference on Data Mining, Workshop on Data Mining in Human Activity Analysis