Journals / Anadolu Üniversitesi Sosyal Bilimler Dergisi / 2023 / Cilt: 23 Sayı: 1

Sosyal Bilimlerde Büyük Veri Analitiği, Yapay Zeka ve Makine Öğreniminin Kullanımı

Pages
99–120
Publication Date
2023-03-28
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗

Özet

Teknolojinin gelişimi ile birlikte sosyal bilimler alanında çalışan araştırmacılara sunulan araç ve tekniklerin sayısı artmaktadır. Büyük hacimli verilerin araştırmalara kolaylıkla entegre edilebilmesine imkan veren ve bu verilerin en doğru ve hızlı şekilde yorumlanmasını sağlayan büyük veri analitiği, yapay zeka ve makine öğrenimi gibi teknikler artık sosyal bilimler alanında daha yaygın biçimde kullanılmaktadır. Bu çalışmanın amacı, günümüz dijital dönüşüm araçları olarak kabul edilen büyük veri analitiği, yapay zeka ve makine öğrenimi gibi kavramların sosyal bilimler araştırmalarındaki kullanım alanlarının belirlenmesi ve bu araçların araştırmacılara sunduğu imkanların tanıtılmasıdır. Bu kapsamda uluslararası alanda yayınlanmış nitelikli araştırmalar incelenerek, söz konusu araçların sosyal bilimler alanındaki bilimsel araştırmalara nasıl uygulandığı, araştırmacılara ne gibi fayda ve avantajlar sağladığı ve gelişim trendleri ile ilgili bir derleme sunulmaktadır. Çalışmada ayrıca söz konusu araçların kullanımından kaynaklı potansiyel sorunlar ele alınarak uluslararası örnekler bağlamında konu tartışılmaktadır.

Keywords
🔖

Cite This Article

Mete, M. H. (2023). Sosyal Bilimlerde Büyük Veri Analitiği, Yapay Zeka ve Makine Öğreniminin Kullanımı. Anadolu Üniversitesi Sosyal Bilimler Dergisi, Cilt: 23 Sayı: 1, 99–120. https://doi.org/10.18037/ausbd.1272565

📄 Full Text PDF Viewer

References (49)

  1. Arnaboldi, M. (2018). The missing variable in big data for social sciences: The decision-maker. Sustainability, 10(3415), 1-18. https://doi.org/10.3390/su10103415
  2. Ateş, E. C., Bostancı, G. E. ve Serdar, M. S. G. (2020). Big data, data mining, machine learning, and deep learning concepts in crime data. Journal of Penal Law and Criminology, 8(2), 293-319. https://doi.org/10.26650/JPLC2020-813328
  3. Beauchamp, N. (2017). Predicting and interpolating state‐level polls using Twitter textual data. American Journal of Political Science, 61(2), 490-503. https://doi.org/10.1111/ajps.12274
  4. Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. Florida: University of South Florida Tampa Library Open Access Collections.
  5. Bostrom, N. ve Yudkowsky, E. (2018). The ethics of artificial intelligence, R.V. Yampolskiy (Ed.), Artificial intelligence safety and security içinde, (s. 57-69). New York: Chapman and Hall/CRC.
  6. Bölükbaşı, T., Chang, K. W., Zou, J. Y., Saligrama, V. ve Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Advances in Neural Information Processing Systems, 29, 1-9. Erişim adresi:https://proceedings.neurips.cc/paper/2016/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf
  7. Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199-231. doi: 10.1214/ss/1009213726
  8. Cai, Y., Ramis Ferrer, B. ve Luis Martinez Lastra, J. (2019). Building university-industry co-innovation networks in transnational innovation ecosystems: Towards a transdisciplinary approach of integrating social sciences and artificial intelligence. Sustainability, 11(17), 1-23. https://doi.org/10.3390/su11174633
  9. Camp, L. J. ve Huber, L. L. (2017). Privacy implications of aware, active, and adaptive technologies, S. Kwon (Ed.), Gerontechnology: Research, practice, and principles in the field of technology and aging içinde, (s. 91-114). New York: Springer.
  10. Cazacu, M. ve Titan, E. (2021). Adapting CRISP-DM for social sciences. BRAIN-Broad Research in Artificial Intelligence and Neuroscience, 11(2Sup1), 99-106. http://dx.doi.org/10.18662/brain/11.2Sup1/97
  11. Chatsiou, K. ve Mikhaylov, S. J. (2020). Deep learning for political science. L. Curini, R. Franzese (Ed.), The SAGE Handbook of Research Methods in Political Science and International Relations (s. 1-52) içinde. https://doi.org/10.4135/9781526486387
  12. Chen, Y., Wu, X., Hu, A., He, G. ve Ju, G. (2021). Social prediction: A new research paradigm based on machine learning. The Journal of Chinese Sociology, 8(1), 1-21. https://doi.org/10.1186/s40711-021-00152-z
  13. Della Porta, D. ve Keating, M. (2008). Approaches and methodologies in the social sciences: A pluralist perspective. Cambridge: Cambridge University Press.
  14. Ertel, W. (2018). Introduction to artificial intelligence. New York: Springer.
  15. Felt, M. (2016). Social media and the social sciences: How researchers employ big data analytics. Big Data & Society, 3(1), 1-15. https://doi.org/10.1177/2053951716645828
  16. Freelon, D. (2014). On the interpretation of digital trace data in communication and social computing research. Journal of Broadcasting & Electronic Media, 58(1), 59-75. https://doi.org/10.1080/08838151.2013.875018
  17. Grimmer, J., Roberts, M. E. ve Stewart, B. M. (2021). Machine learning for social science: An agnostic approach. Annual Review of Political Science, 24, 395-419. https://doi.org/10.1146/annurev-polisci-053119-015921
  18. Hamet, P. ve Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69(S), 36-40. https://doi.org/10.1016/j.metabol.2017.01.011
  19. Haydam, N. E. ve Steenkamp, P. (2020). A methodological blueprint for social sciences research–the social sciences research methodology framework. EIRP Proceedings, 15(1), 304-325. Erişim adresi: https://proceedings.univ-danubius.ro/index.php/eirp/article/view/2026
  20. Helbing, D., Frey, B. S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., ...ve Zwitter, A. (2019). Will democracy survive big data and artificial intelligence? Towards Digital Enlightenment (s. 73-98) içinde. Cham: Springer.
  21. Herzig, A., Lorini, E. ve Pearce, D. (2019). Social intelligence. AI & Society, 34(4), 689-689. https://doi.org/10.1007/s00146-017-0782-8
  22. Karthika, S., Kiruthiga, A. ve Bose, S. (2012). Behavioral profile generation for 9/11 terrorist network using efficient selection strategies. D.C. Wyld, J. Zizka, D. Nagamalai (Eds.) Advances in Computer Science, Engineering & Applications (s. 333-344) içinde. New Delhi: Springer.
  23. Khatri, A., Singh, N. P. ve Gupta, N. (2021). Big data analytics: Direction and impact on financial technology. Journal of Management, Marketing and Logistics (JMML), 8(4), 218-234. https://doi.org/10.17261/Pressacademia.2021.1529
  24. Kong, M., Li, L., Wu, R. ve Tao, X. (2021). An empirical study of learning based happiness prediction approaches. Human-Centric Intelligent Systems, 1(1-2), 18-24. https://doi.org/10.2991/hcis.k.210622.001
  25. Kulesza, T., Stumpf, S., Wong, W. K., Burnett, M. M., Perona, S., Ko, A. J. ve Oberst, I. (2011). Why-oriented end-user debugging of naive Bayes text classification. ACM Transactions on Interactive Intelligent Systems (TiiS), 1(1), 1-31. https://doi.org/10.1145/2030365.2030367
  26. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., ...ve Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721-723. doi: 10.1126/science.1167742
  27. Liao, H. T., Wang, Z. ve Liu, Y. (2020). Exploring the cross-disciplinary collaboration: A scientometric analysis of social science research related to artificial intelligence and big data application. IOP Conf. Series: Materials Science and Engineering (Vol. 806) içinde, (s. 1-6). doi: 10.1088/1757-899X/806/1/012019
  28. Ligo, A. K., Rand, K., Bassett, J., Galaitsi, S. E., Trump, B. D., Jayabalasingham, B., Collins, T. ve Linkov, I. (2021). Comparing the emergence of technical and social sciences research in artificial intelligence. Frontiers in Computer Science, 3, 1-13. https://doi.org/10.3389/fcomp.2021.653235
  29. Lindgren, S. ve Holmström, J. (2020). A social science perspective on artificial intelligence: Building blocks for a research agenda. Journal of Digital Social Research (JDSR), 2(3), 1-15. https://doi.org/10.33621/jdsr.v2i3.65
  30. Martin, T., Hofman, J. M., Sharma, A., Anderson, A. ve Watts, D. J. (2016). Exploring limits to prediction in complex social systems. WWW’16: Proceedings of the 25th International Conference on World Wide Web (s. 683-694) içinde. https://doi.org/10.1145/2872427.2883001
  31. Martinho, T. D. (2018). Researching culture through big data: Computational engineering and the human and social sciences. Social Sciences, 7(264), 1-17. https://doi.org/10.3390/socsci7120264
  32. McCormick, T. H., Lee, H., Cesare, N., Shojaie, A. ve Spiro, E. S. (2017). Using Twitter for demographic and social science research: Tools for data collection and processing. Sociological Methods & Research, 46(3), 390-421. https://doi.org/10.1177/0049124115605339
  33. Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1-38. https://doi.org/10.1016/j.artint.2018.07.007
  34. Mondal, M. (2018). Research methodology in social sciences: Qualitative research approach. Voice of Research, 7(3), 21-24. Erişim adresi: http://voiceofresearch.org/Doc/Dec-2018/Dec-2018_8.pdf
  35. Nascimento, F. M. D. (2020). Contributions of machine learning to knowledge acquisition in the field of social sciences (Yüksek lisans tezi, Federal University of Rio Grande do Sul, Porto Alegre). Erişim adresi: https://lume.ufrgs.br/handle/10183/217575
  36. Olteanu, A., Castillo, C., Diaz, F. ve Kıcıman, E. (2019). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, 2(13), 1-33. https://doi.org/10.3389/fdata.2019.00013
  37. Pukelis, L. ve Stanciauskas, V. (2019). The opportunities and limitations of using artificial neural networks in social science research. Politologija, 94(2), 56-80. https://doi.org/10.15388/Polit.2019.94.2
  38. Radford, J. ve Joseph, K. (2020). Theory in, theory out: The uses of social theory in machine learning for social science. Frontiers in Big Data, 3(18), 1-13. https://doi.org/10.3389/fdata.2020.00018
  39. Reier Forradellas, R. F., Nanez Alonso, S. L., Jorge-Vazquez, J. ve Rodriguez, M. L. (2020). Applied machine learning in social sciences: Neural networks and crime prediction. Social Sciences, 10(4), 1-20. https://doi.org/10.3390/socsci10010004
  40. Rezaev A. V. ve Tregubova N. D. (2018) Are sociologists ready for ‘artificial sociality’? Current issues and future prospects for studying artificial intelligence in the social sciences. Monitoring of Public Opinion: Economic and Social Changes, 5, 91-108. https://doi.org/10.14515/monitoring.2018.5.10
  41. Robila, M. ve Robila, S. A. (2020). Applications of artificial intelligence methodologies to behavioral and social sciences. Journal of Child and Family Studies, 29(10), 2954-2966. https://doi.org/10.1007/978-3-031-19922-6_2
  42. Rudin, C. ve Wagstaff, K. L. (2014). Machine learning for science and society. Machine Learning, 95(1), 1-9. https://doi.org/10.1007/s10994-013-5425-9
  43. Russell, S. J. ve Norvig, P. (2010). Artificial intelligence: A modern approach. New Jersey: Pearson Education.
  44. Sağıroglu, S. ve Sinanc, D. (2013). Big data: A review. International Conference on Collaboration Technologies and Systems (CTS) (s. 42-47) içinde. IEEE. doi: 10.1109/CTS.2013.6567202.
  45. Shu, X. (2020). Knowledge discovery in the social sciences: A data mining approach. Oakland: University of California Press. https://doi.org/10.2307/j.ctvw1d683
  46. Ünal, S. ve Sezgin, A. A. (2021). Büyük veri (big data)’nin yapay zeka uygulamalarında toplumsal sınıflandırmaya yönelik kaygılar. AJIT-e: Bilişim Teknolojileri Online Dergisi, 12(44), 47-70. https://doi.org/10.5824/ajite.2021.01.004.x
  47. Van Lohuizen, J. ve Samohyl, R. W. (2011). Method effects and robo-polls. Survey Practice, 4(1), 1-9. https://doi.org/10.29115/SP-2011-0005
  48. Wu, X. ve Zhang, X. (2016). Automated inference on criminality using face images. (Addendum of arXiv: 1611.04135). Computer Science, 0(0). 1-11. https://doi.org/10.48550/arXiv.1611.04135
  49. Zhang, J. ve Feng, S. (2021). Machine learning modeling: A new way to do quantitative research in social sciences in the era of AI. Journal of Web Engineering, 20(2), 281-302. https://doi.org/10.13052/jwe1540-9589.2023