Computational social science

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Computational social science is an interdisciplinary academic sub-field concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. It has been applied in areas such as computational economics, computational sociology, computational media analysis, cliodynamics, culturomics, nonprofit studies. [1] It focuses on investigating social and behavioral relationships and interactions using data science approaches (such as machine learning or rule-based analysis), network analysis, social simulation and studies using interactive systems. [2]

Contents

Definitions

There are two terminologies that relate to each other: Social Science Computing (SSC) and Computational Social Science (CSS). In literature, CSS is referred to the field of social science that uses the computational approaches in studying the social phenomena. On the other hand, SSC is the field in which computational methodologies are created to assist in explanations of social phenomena.

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. [3] [4] 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, [5] 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 analysis of vast quantities of historical newspaper [11] and book content [12] have been pioneered in 2017, while other studies on similar data [13] showed how periodic structures can be automatically discovered in historical newspapers. A similar analysis was performed on social media, again revealing strongly periodic structures. [14]

Approaches

As an interdisciplinary area, scholars come from many different established fields. However, there seems to be a shared ethos among them that the field ought to integrate knowledge across traditional scholarly boundaries. [15] [16] However, Nelimarkka [17] proposes that five distinct archetypal approaches to computational social science:

Overall, computational social science is a diverse academic enterprise. There are some scholarly works, particularly from computer science which seem to hold the discipline together, but beyond that there are more diverse communities. [18]

Academic publication avenues

Computational social science articles are published across several journals, such as New Media & Society, Social Science Computer Review, PNAS, Political communication, EPJ Data Science, PloS One, Sociological Methods & Research and Science. [19]

However, there are some venues focused only in computational social sciences:

See also

Related Research Articles

<span class="mw-page-title-main">Social science</span> Branch of science that studies society and its relationships

Social science is one of the branches of science, devoted to the study of societies and the relationships among individuals 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, human geography, linguistics, management science, communication science, psychology and political science.

Computational archaeology describes computer-based analytical methods for the study of long-term human behaviour and behavioural evolution. As with other sub-disciplines that have prefixed 'computational' to their name, the term is reserved for methods that could not realistically be performed without the aid of a computer.

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) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a knowledge discovery in databases (KDD) process. 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.

<span class="mw-page-title-main">Social complexity</span> Conceptual framework

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.

<span class="mw-page-title-main">Computational sociology</span> Branch of the discipline of sociology

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.

Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science that uses advanced computing capabilities to understand and solve complex physical problems. This includes

<span class="mw-page-title-main">Macrosociology</span> Sociological theories and approaches that focus on large-scale aspects of society

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.

<span class="mw-page-title-main">Digital humanities</span> Area of scholarly activity

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.

<span class="mw-page-title-main">Peter Turchin</span> American quantitative historian (born 1957)

Peter Valentinovich Turchin is a Russian-American complexity scientist, specializing in an area of study he and his colleagues developed called cliodynamics—mathematical modeling and statistical analysis of the dynamics of historical societies.

<span class="mw-page-title-main">Historical sociology</span> Interdisciplinary field of research

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.

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.

<span class="mw-page-title-main">Nigel Gilbert</span>

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.

The social data revolution is the shift in human communication patterns towards increased personal information sharing and its related implications, made possible by the rise of social networks in the early 2000s. This phenomenon has resulted in the accumulation of unprecedented amounts of public data.

<span class="mw-page-title-main">Kathleen Carley</span> American social scientist

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.

<span class="mw-page-title-main">Seshat (project)</span>

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.

References

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  3. DT&SC 7-1: . Introduction to e-Science: From the DT&SC online course at the University of California
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  6. Turchin, Peter; Brennan, Rob; Currie, Thomas E.; Feeney, Kevin C.; Francois, Pieter; Hoyer, Daniel; Manning, J. G.; Marciniak, Arkadiusz; Mullins, Daniel; Palmisano, Alessio; Peregrine, Peter; Turner, Edward A. L.; Whitehouse, Harvey (2015). "Seshat: The Global History Databank" (PDF). Cliodynamics. 6: 77. https://escholarship.org/uc/item/9qx38718
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  12. Roth, Steffen; et al. (2017). "Futures of a distributed memory. A global brain wave measurement (1800-2000)". Technological Forecasting and Social Change. 118: 307–323. doi:10.1016/j.techfore.2017.02.031. S2CID   67011708.
  13. Dzogang, Fabon; Lansdall-Welfare, Thomas; Team, FindMyPast Newspaper; Cristianini, Nello (2016-11-08). "Discovering Periodic Patterns in Historical News". PLOS ONE. 11 (11): e0165736. Bibcode:2016PLoSO..1165736D. doi: 10.1371/journal.pone.0165736 . ISSN   1932-6203. PMC   5100883 . PMID   27824911.
  14. 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
  15. Wallach, H. (2018). Computational social science ≠ computer science + social data. Communications of the ACM, 61(3), 42–44. https://doi.org/10.1145/3132698
  16. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Social science. Computational social science. Science, 323, 721–723. https://doi.org/10.1126/science.1167742
  17. Nelimarkka, M. (2023). Computational Thinking and Social Science: Combining Programming, Methodologies and Fundamental Concepts. SAGE Publishing.
  18. Wang, X., Song, Y., & Su, Y. (2023). Less Fragmented but Highly Centralized: A Bibliometric Analysis of Research in Computational Social Science. Social Science Computer Review, 41(3), 946–966. https://doi.org/10.1177/08944393211058112
  19. Based on reviews on the literature, see for example Wang, X., Song, Y., & Su, Y. (2023). Less Fragmented but Highly Centralized: A Bibliometric Analysis of Research in Computational Social Science. Social Science Computer Review, 41(3), 946–966. https://doi.org/10.1177/08944393211058112 and Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020). Computational Social Science and Sociology. Annual Review of Sociology, 46(1), annurev-soc-121919-054621. https://doi.org/10.1146/annurev-soc-121919-054621