Hesaplamalı Sosyal Bilimlerden Ne Anlamalıyız? Bir Literatür İncelemesi

Year 2024, Volume: 1 Issue: 2, 190 - 209, 17.07.2024

Hasan Tutar , Esra Ayaz

Abstract

Hesaplamalı Sosyal Bilimler, karmaşık sosyal olguları analiz etmek ve anlamak için hesaplamalı yöntemlerden ve veri odaklı yaklaşımlardan yararlanan disiplinler arası bir alanı temsil etmektedir. Bu literatür incelemesi, Hesaplamalı Sosyal Bilimler alanındaki önemli gelişmeler, metodolojiler ve uygulamalar hakkında genel bir bakış sağlamayı amaçlamaktadır. Araştırmada başlangıçtan bugüne yayınlanan ve çeşitli akademik veri tabanlarından elde edilen bilimsel makaleler, kitaplar ve araştırma makaleleri üzerinde sistematik bir inceleme gerçekleştirilmiştir. İnceleme, Hesaplamalı Sosyal Bilimlerin ilk aşamalarından olgun bir alana doğru evrimini vurgulamakta ve ajan tabanlı modelleme, ağ analizi, makine öğrenimi ve doğal dil işleme dahil olmak üzere çeşitli hesaplama tekniklerini tartışmaktadır. Kısaca Hesaplamalı Sosyal Bilimler, sosyal bilimler alanının geleneksel yöntemlerini genişletip güçlendirerek sosyal sistemlerin daha iyi bir şekilde anlaşılmasına ve yönetilmesine katkıda bulunan bir disiplin olarak görülmektedir. Hesaplamalı Sosyal Bilimler, karmaşık sosyal sistemler hakkında bir anlayış kazanmak için güçlü bir araç olarak gelişmeye devam etmektedir.

Keywords

Hesaplamalı Sosyal Bilimler, Literatür Taraması, Doğal Dil İşleme, Bilgisayar Bilimleri, Sosyal Fenomenler

Supporting Institution

TUBİTAK

Project Number

122G157

Thanks

Bu makale TÜBİTAK tarafından desteklenen 122G157 numaralı proje kapsamında üretilmiş bir makaledir ve desteğinden dolayı TÜBİTAK’a ve sayın çalışanlarına çok teşekkür ederiz.

What Should We Understand from Computational Social Sciences? A Literature Review

Abstract

Computational Social Science represents an interdisciplinary field that leverages computational methods and data-driven approaches to analyze and understand complex social phenomena. This literature review aims to overview key developments, methodologies, and applications in Computational Social Science. A systematic review of scholarly articles, books, and research papers published since its inception and retrieved from various academic databases was conducted. The review highlights the evolution of Computational Social Science from its early stages to a mature field and discusses various computational techniques, including agent-based modeling, network analysis, machine learning, and natural language processing. In short, Computational Social Science is seen as a discipline that contributes to a better understanding and management of social systems by extending and strengthening the traditional methods of the social sciences. Computational Social Science continues to evolve as a powerful tool for understanding complex social systems.

Keywords

Computational Social Science, Literature Review, Natural Language Processing, Computer Science, Social Phenomena

Project Number

122G157

References

• Abrahao, B. & Parigi, P. (2020). Computational social science, big data, and networks. In: Light, R. & Moody, J. (Ed.) The Oxford handbook of social networks, içinde (s. 516-134). Oxford, UK: Oxford University Press
• Amballoor, R. & Naik, S. (2020). Optimizing the value of big data: role of computational social science. Topics in Intelligent Computing and Industry Design (ICID) 2(1), 118-120, https://doi.org/10.26480/cic.01.2020.118.120.
• Bedru, H. D., Yu, S., Xiao, X., Zhang, D., Wan, L., Guo, H. & Xia, F., (2020). Big networks: a survey. Computer Science Review, 37, https://doi.org/10.1016//j.cosrev.2020.100247.
• Bonaventura, L. & Consoli, A. (2013). Priorities for Backlog of Criminal Cases Pending in Courts: A Computational Agent-based Model, forthcoming in Faro, Lettieri (ed.), Law and Computational Social Science, special issue of the journal Informatica e Diritto, 1.
• Bosetti, P., Poletti, P., Stella, M., Lepri, B., Merler, S. & De Domenico, M. (2019). Reducing measles risk in Turkey through social integration of Syrian refugees. ArXiv, abs/1901.04214.
• Bosse, T. & Gerritsen, C. (2010). Social Simulation and Analysis of the Dynamics of Criminal Hot Spots. Journal of Artificial Societies and Social Simulation, 13(2), http://jasss.soc.surrey.ac.uk/13/2/5.html.
• Boulet, R., Mazzega, P. & Bourcier, D. (2010). Network Analysis of the French Environmental Code, in Casanovas, Pagallo, Sartor, Ajani (Ed.), AI Approaches to the Complexity of Legal Systems içinde (s. 39-53), Heidelberg.
• Cameron, M. A., Power, R., Robinson, B. & Yin, J., (2012). Emergency situation awareness from Twitter for crisis management. Proceedings of the 21st International Conference on World Wide Web, 695-698.
• Cioffi Revilla, C. (2010). Computational social science. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 259-271, https://doi.org/10.1002/wics.95.
• Cioffi Revilla, C. (2014). Introduction to Computational Social Science: Principles and Applications. Springer.
• Coleman, J. S. (1986). Social theory, social research, and a theory of action. American Journal of Sociology, 91(6), 1309-1335, https://doi.org/10.1086/228423.
• Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., Loreto, V., Moat, S., Nadal, J. P., Sanchez, A., Nowak, A., Flache, A., San Miguel, M. & Helbing, D. (2012). Manifesto of computational socialscience, The European Physical Journal Special Topics, 214, 325-346, doı: 10.1140/epjst/e2012-01697-8.
• De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G. & Kumar, M. (2016). Discovering shifts to suicidal ideation from mental health content in social media. In Proceedings of the 2016 CHI conference on human factors in computing systems, 2098-2110, https://doi.org/10.1145/2858036.2858207.
• Edelmann, A., Wolff, T., Montagne, D. & Bail, C. A. (2020). Computational social science and sociology. Annual Review of Sociology, 46(1), 61-81, doı:10.1146/annurev-soc-121919-054621.
• Gonzalez-Bailon, S., Borge-Holthoefer, J., Rivero, A. & Moreno, Y. (2011). The dynamics of protest recruitment through an online network. Scientific Reports, 1, https://doi.org/10.1038/srep00197.
• Groves, R. (2011). Designed Data and Organic Data. Director’s Blog, US Census Bureau, http://directorsblog.blogs.census.gov/2011/05/31/designeddata-and-organic-data.
• Helic, D. & Strohmaier, M. (2011). Building Directories for Social Tagging Systems. Proc. 20th ACM Conf. Information and Knowledge Management, 525-534.
• Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S. & Vespignani, A. (2021). Integrating explanation and prediction in computational social science. Nature, 595(7866), 181-188, doı: 10.1038/s41586-021-03659-0.
• Jarvis, F. B., Keuschnigg, M. & Hedström, P. (2022). Analytıcal socıology amıdst a computatıonal socıal scıence revolutıon. Handbook Of Computatıonal Socıal Scıence, Case Studies and Ethics, doi:10.4324/9781003024583-4.
• Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J. & Christakis, N. A. (2020). Population flow drives spatio-temporal distribution of COVID-19 in China, Nature, 582(7812), 389-394, https://doi.org/10.1038/s41586-020-2284-y.
• Kiciman, E., Counts, S. & Gasser, L. (2017). Leveraging social media for behavioral psychology research: The possibilities and challenges. In Proceedings of the 2017 CHI conference on human factors in computing systems, 2254-2267.
• Lazer, D., Kennedy, R., King, G. & Vespignani, A. (2014). The parable of google flu: traps in big data analysis. Science, 343(6176), 1203-1205, https://doi.org/10.1126/science.1248506.
• Lazer, D., Pentland, A., Adamic, L., Aral, S., Barab´asi, A., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Roy, D., Macy, M.W. & Van Alystyn, M. (2009). Computational social science. Science, 323(5915), 721-723, https://doi.org/10.1126/science.1167742.
• Lee, R. M. (2000). Unobtrusive Methods in Social Research. Open Univ. Press.
• Lehmann-Willenbrock, N., Hung, H. & Keyton, J. (2017). New frontiers in analyzing dynamic group interactions: bridging social and computer science. Small Group Research, 48(5), 519-531, https://doi.org/10.1177/1046496417718941.
• Lerman, K. & Hogg, T. (2010). Using a model of social dynamics to predict popularity of news. Proceedings of the National Academy of Sciences, 107(47), 197-201, https://doi.org/10.1145/1772690.1772754.
• Lettieri, N. & Faro, S. (2012). Computational Social Science and its Potential Impact upon Law. European Journal of Law and Technology, 3(3).
• Liu, J., Tang, T., Wang, W., Xu, B., Kong, X. & Xia, F. (2018). A survey of scholarly data visualization. IEEE Access 6(1), 19205-19221, https://doi.org/10.1109//ACCESS.2018.2815030.
• Mann, A. (2016). Core concept: Computational social science. Proceedings of the National Academy of Sciences, 113(3), 468-470, doı:10.1073/pnas.1524881113.
• Mason, W., Vaughan, W. J. & Wallach, H. (2014). Computational social science and social computing. Mach Learn, 95, 257-260, doı: 10.1007/s10994-013-5426-8
• Metaxas, P. & Mustafaraj, E. (2014). Sifting the sand on the river bank: social media as a source for research data. Information Technology, 56(5), 230-239, https://doi.org/10.1515/itit-2014-1047.
• Mogos, A., Mogos, B. & Florea, A. (2015). A voting approach for comparing several swarm intelligence algorithms. 20th International Conference on Control Systems and Computer Science, Bucharest, Romania, https://doi.org/10.1109/cscs.2015.134.
• Muñoz, J. & Young, C. (2018). We Ran 9 billion regressions: eliminating false positives through computational model robustness. Sociological Methodology, 48(1), 1-33.
• Neuman, W. L. (2014). Toplumsal Araştırma Yöntemleri Nitel ve Nitel Yaklaşımlar. (Çev. Sedef Özge). Ankara: Yayın Odası.
• Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., Deletaille, S., De Nadai, M., Letouzé, E., Salah, A. A., Benjamins, R., Cattuto, C., Colizza, V. & Vinck, P. (2020). Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science advances, 6(23), https://doi.org/10.1126/sciadv.abc0764.
• Preece, J. (2001). Sociability and Usability in Online Communities: Determining and Measuring Success. Behaviour and Information Technology, 20(5), 347-356, http://dx.doi.org/10.1080/01449290110084683.
• Sagarra, O., Gutiérrez-Roig, M., Bonhoure, I. & Perelló, J. (2016). Citizen Science Practices for Computational Social Science Research: The Conceptualization of Pop Up Experiments. Front. Phys. 3(93), doi: 10.3389/fphy.2015.00093.
• Salah, A. A. (2023). Hesaplamalı sosyal bilimler nedir?. https://sarkac.org/2023/01/hesaplamali-sosyal-bilimler-nedir/ (Erişim tarihi: 26.08.2023).
• Salganik, M. J. & Watts, D. J. (2018). Introduction to computational social science: Principles and applications. Princeton University Press.
• Seshadhri, C., Sharma, A., Stolman, A. & Goel, A. (2020). The impossibility of low-rank representations for triangle-rich complex networks. Proceedings of the National Academy of Sciences, 117(11), 5631-5637. https://doi.org/10.1073/pnas.1911030117.
• Snee, H., Hine, C., Morey, Y., Roberts, S. & Watson, H. (2016). Ana Akım Yöntembilim Olarak Dijital Yöntemler: Giriş. (Çev. S. Ersöz Karakulakoğlu), Sosyal Bilimler İçin Dijital Yöntemler Yöntemsel Yenilikler İçin Disiplinlerarası Bir Kılavuz, içinde (s. 1-12), Nobel Akademik Yayıncılık.
• Strohmaier, M. & Wagner, C. (2014). Computational Social Science for the World Wide Web. IEEE Computer Society, 84-88.
• Strohmaier, M. (2013). A Few Thoughts on Engineering Social Machines, Proc. 2013 World Wide Web Conf., www.markusstrohmaier.info/documents/a_few_thoughts_on_engineering_social_machines.pdf.
• Tindall, D., McLevey, J., Koop-Monteiro, Y. & Graham, A. (2022). Big data, computational social science, and other recent innovations in social network analysis. Canadian Review of Sociology, 59, 271-288, https://doi.org/10.1111/cars.12377.
• Toole, J. L., Lin, Y. R., Muehlegger, E., Shoag, D., González, M. C. & Lazer, D. (2015). Tracking employment shocks using mobile phone data. Journal of the Royal Society Interface, 12(107), http://dx.doi.org/10.1098/rsif.2015.0185.
• Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In Eighth International Conference on Weblogs and Social Media, 505-514, https://doi.org/10.1609/icwsm.v8i1.14517
• Watts, D. J. (2011). Computational social science: Exciting progress and future directions. The Bridge on Social Science and Technology, 41(4), 9-10, doı:10.1145/2939672.2945366.
• Webb, E. J., Campbell, D. T., Schwartz, R. D. & Sechrest, L. (1966). Unobtrusive measures: Nonreactive research in the social sciences, Rand Mcnally.
• Weber, I., Wagner, C., Strohmaier, M. & Aiello, L. (2016). Computational social science for the world wide web (cssw3), WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web, 1037-1038, https://doi.org/10.1145/2872518.2891062.
• Xia, F., Wei, H., Yu, S., Zhang, D. & Xu, B. (2019). A survey of measures for network motifs. IEEE Access 7(1), 106576–106587, https://doi.org/10.1109//ACCESS.2019.2926752.
• Zhang, J., Wang, W., Xia, F., Lin, Y. & Tong, H. (2020). Data-Driven Computational Social Science: A Survey, Big Data Research 21, 100145, https://doi.org/10.1016/j.bdr.2020.100145.