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SOSYAL BİLİMLERDE VERİ BİLİMİ KULLANIMI

Year 2023, Volume: 10 Issue: 4, 1175 - 1207, 18.10.2023

Fatih Sağlam Mehmet Ali Cengiz

Abstract

Bu çalışma, veri biliminin sosyal bilimler alanındaki önemli rolünü ve uygulamalarını ele almaktadır. Geleneksel sosyal bilim yaklaşımlarıyla kıyaslandığında, veri bilimi yöntemleri geniş veri kütlelerini etkili bir şekilde analiz etme ve değerli içgörüler elde etme potansiyeli sunmaktadır. Bu özelleşmiş disiplinler arası yaklaşımın, sosyal ağ analizinden duygu analizine, büyük veri analizinden tahmin modellerine kadar geniş bir yelpazede nasıl kullanıldığı incelenmektedir. Veri bilimi, sosyal bilimlerde yeni bir ufuk açmıştır. Bu yaklaşım, geleneksel sosyal bilim yöntemlerinin sınırlarını aşarak, daha kapsamlı ve derinlemesine analizler yapmayı mümkün kılmaktadır. Veri bilimi yöntemleri, sosyal ağların yapısını ve dinamiklerini daha iyi anlamamıza, kamuoyu görüşlerini daha doğru bir şekilde ölçmemize ve sosyal problemlerin kökenlerini daha iyi tespit etmemize yardımcı olmaktadır. Ancak, veri bilimi ile sosyal bilimlerin entegrasyonunun bazı zorluklar ve etik konular da beraberinde getirdiğini belirtmek gerekir. Verilerin toplanması, analizi ve yorumlanması sırasında ortaya çıkabilecek önyargı ve ayrımcılık riskleri, bu alandaki çalışmalara dikkatli bir şekilde yaklaşılmasını gerektirmektedir. Gelecekte, veri biliminin sosyal bilimler alanındaki kullanımının daha da yaygınlaşması beklenmektedir. Bu inceleme, gelecekteki yönelimler ve beklenen katkılar da ele alınarak, veri biliminin sosyal bilimler alanındaki rolü hakkında daha iyi bir anlayış sunmaktadır.

Keywords

veri bilimi sosyal bilimler veri analizi sosyal ağ analizi büyük veri tahmin modelleri

Ethical Statement

Etik dışı bir durum sözkonusu değildir.

Supporting Institution

Destekleyen kurum yok

Thanks

yok

References

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  • Li, T., Zeng, Z., Sun, J., ve Sun, S. (2022). Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media. The Electronic Library, 40(4), 435-452.
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Year 2023, Volume: 10 Issue: 4, 1175 - 1207, 18.10.2023

Fatih Sağlam Mehmet Ali Cengiz

Abstract

References

  • Addagarla, S. K., ve Amalanathan, A. (2020). Probabilistic unsupervised machine learning approach for a similar image recommender system for E-commerce. Symmetry, 12(11), 1783.
  • Asadi, F., Trinugroho, J. P., Hidayat, A. A., Rahutomo, R., ve Pardamean, B. (2023). Data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors. Procedia Computer Science, 216, 284-292.
  • Akour, M., Alsghaier, H., ve Al Qasem, O. (2020). The effectiveness of using deep learning algorithms in predicting students achievements. Indonesian Journal of Electrical Engineering and Computer Science, 19(1), 387-393.
  • Albayrak, M., Topal, K., ve Altıntaş, V. (2017). Sosyal medya üzerinde veri analizi: Twitter. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(Kayfor 15 Özel Sayısı), 1991-1998.
  • Ashtiani, M. N., ve Raahmei, B. (2023). News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review. Expert Systems with Applications, 119509.
  • Bayes, T. (1763). LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S. Philosophical transactions of the Royal Society of London, (53), 370-418, https://doi.org/10.1098/rstl.1763.0053.
  • Bennear, L. S., ve Coglianese, C. (2005). Measuring progress: program evaluation of environmental policies. Environment: Science and Policy for Sustainable Development, 47(2), 22-39.
  • Best, K., Gilligan, J., Baroud, H., Carrico, A., Donato, K., ve Mallick, B. (2022). Applying machine learning to social datasets: a study of migration in southwestern Bangladesh using random forests. Regional Environmental Change, 22(2), 52.
  • Bokhare, A., ve Kothari, T. (2023). Emotion Detection-Based Video Recommendation System Using Machine Learning and Deep Learning Framework. SN Computer Science, 4(3), 215.
  • Bonetti, A., Martínez-Sober, M., Torres, J. C., Vega, J. M., Pellerin, S., ve Vila-Francés, J. (2023). Comparison between Machine Learning and Deep Learning Approaches for the Detection of Toxic Comments on Social Networks. Applied Sciences, 13(10), 6038.
  • Brayne, S., ve Christin, A. (2021). Technologies of crime prediction: The reception of algorithms in policing and criminal courts. Social Problems, 68(3), 608-624.
  • Choi, S., Hong, J. Y., Kim, Y. J., ve Park, H. (2020). Predicting psychological distress amid the COVID-19 pandemic by machine learning: discrimination and coping mechanisms of Korean immigrants in the US. International journal of environmental research and public health, 17(17), 6057.
  • Dangol, R., ve Shrestha, M. (2019). Learning readiness and educational achievement among school students. The International Journal of Indian Psychology, 7(2), 467-476.
  • Farsi, M., Daneshkhah, A., Far, A. H., Chatrabgoun, O., ve Montasari, R. (2018). Crime data mining, threat analysis and prediction. In H. Jahankani (Eds.), Cyber Criminology, (pp. 183-202). Springer.
  • Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., ve Van Erven, G. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of business research, 94, 335-343.
  • Halkiopoulos, C., Gkintoni, E., ve Antonopoulou, H. (2020). Behavioral data analysis in emotional intelligence of social network consumers. British Journal of Marketing Studies (BJMS), 8 (2), 26-34.
  • He, M., Ma, C., ve Wang, R. (2022). A Data-Driven Approach for University Public Opinion Analysis and Its Applications. Applied Sciences, 12(18), 9136.
  • Hongliang, C., ve Xiaona, Q. (2015, October). The video recommendation system based on DBN. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (pp. 1016-1021). IEEE.
  • Huang, A., Qiu, L., ve Li, Z. (2021). Applying deep learning method in TVP-VAR model under systematic financial risk monitoring and early warning. Journal of Computational and Applied Mathematics, 382, 113065.
  • Guyon, I., Boser, B., ve Vapnik, V. (1992). Automatic capacity tuning of very large VC-dimension classifiers. Advances in neural information processing systems, 5.
  • Jacobucci, R., ve Grimm, K. J. (2020). Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science, 15(3), 809-816.
  • Injadat, M., Moubayed, A., Nassif, A. B., ve Shami, A. (2020). Systematic ensemble model selection approach for educational data mining. Knowledge-Based Systems, 200, 105992.
  • Khan, S. A. R., Zhang, Y., Kumar, A., Zavadskas, E., ve Streimikiene, D. (2020). Measuring the impact of renewable energy, public health expenditure, logistics, and environmental performance on sustainable economic growth. Sustainable development, 28(4), 833-843.
  • Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., ve Herrera Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors.
  • Kruppa, J., Ziegler, A., ve König, I. R. (2012). Risk estimation and risk prediction using machine-learning methods. Human genetics, 131, 1639-1654.
  • Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., ve Liu, J. (2020). Projecting the potential impact of COVID-19 school closures on academic achievement. Educational Researcher, 49(8), 549-565.
  • Kumar, S., Sharma, D., Rao, S., Lim, W. M., ve Mangla, S. K. (2022). Past, present, and future of sustainable finance: insights from big data analytics through machine learning of scholarly research. Annals of Operations Research, 1-44.
  • Legendre, A. M. (1787). Mémoire sur les opérations trigonométriques: dont les résultats dépendent de la figure de la terre, Mèmoires de 1’Acadèmie des Sciences de Paris, 352-383, https://books.google.com.tr/books?id=0uIEAAAAQAAJ&pg=PA352
  • Lettieri, N., Guarino, A., Malandrino, D., ve Zaccagnino, R. (2022). Knowledge mining and social dangerousness assessment in criminal justice: metaheuristic integration of machine learning and graph-based inference. Artificial Intelligence and Law, 1-50.
  • Li, T., Zeng, Z., Sun, J., ve Sun, S. (2022). Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media. The Electronic Library, 40(4), 435-452.
  • Loukides, M., Mason, H., ve Patil, D. J. (2018). Ethics and data science. O'Reilly Media.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133. https://link.springer.com/content/pdf/10.1007/BF02478259.pdf
  • Mogaveera, D., Mathur, V., ve Waghela, S. (2021, January). e-Health monitoring system with diet and fitness recommendation using machine learning. In 2021 6th International Conference on Inventive Computation Technologies (ICICT) (pp. 694-700). IEEE.
  • Mohsin, M., Kamran, H. W., Nawaz, M. A., Hussain, M. S., ve Dahri, A. S. (2021). Assessing the impact of transition from nonrenewable to renewable energy consumption on economic growth-environmental nexus from developing Asian economies. Journal of environmental management, 284, 111999.
  • Pang, B., Lee, L., ve Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing. https://doi.org/10.3115/1118693.1118704
  • Parveen, R., ve Varma, N. S. (2021). Friend's recommendation on social media using different algorithms of machine learning. Global Transitions Proceedings, 2(2), 273-281.
  • Patil, D. J. (2011). Building data science teams. " O'Reilly Media, Inc.".
  • Patil, D. J., ve Mason, H. (2015). Data Driven. “O'Reilly Media, Inc.”.
  • Pisarevskaya, A., Levy, N., Scholten, P., ve Jansen, J. (2020). Mapping migration studies: an empirical analysis of the coming of age of a research field. Migration studies, 8(3), 455-481.
  • Prathap, B. R., Krishna, A. V., ve Balachandran, K. (2021). Crime analysis and forecasting on spatio temporal news feed data—an indian context. In Artificial intelligence and blockchain for future cybersecurity applications (pp. 307-327). Cham: Springer International Publishing.
  • Reese, K. M. (2021). Deep learning artificial neural networks for non-destructive archaeological site dating. Journal of Archaeological Science, 132, 105413.
  • Richardson, R., Schultz, J. M., ve Crawford, K. (2019). Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. NYUL Rev. Online, 94, 15.
  • Saravanan, P., Selvaprabu, J., Arun Raj, L., Abdul Azeez Khan, A., ve Javubar Sathick, K. (2021). Survey on crime analysis and prediction using data mining and machine learning techniques. In Advances in Smart Grid Technology: Select Proceedings of PECCON 2019—Volume II (pp. 435-448). Springer Singapore.
  • Shahbazi, Z., ve Byun, Y. C. (2020). Toward social media content recommendation integrated with data science and machine learning approach for E-learners. Symmetry, 12(11), 1798.
  • Shen, L., ve Xu, M. (2022). Student public opinion management in campus commentary based on deep learning. Wireless Communications and Mobile Computing, 2022.
  • Singh, M., Bansal, D., ve Sofat, S. (2016). Behavioral analysis and classification of spammers distributing pornographic content in social media. Social Network Analysis and Mining, 6, 1-18.
  • Singh, D. K., Nithya, N., Rahunathan, L., Sanghavi, P., Vaghela, R. S., Poongodi, M., Hamdi, M., & Tunze, G. B. (2022). Social network analysis for precise friend suggestion for Twitter by associating multiple networks using ML. International Journal of Information Technology and Web Engineering, 17(1), 1–11. https://doi.org/10.4018/ijitwe.304050
  • Song, M., An, Q., Zhang, W., Wang, Z., ve Wu, J. (2012). Environmental efficiency evaluation based on data envelopment analysis: A review. Renewable and Sustainable Energy Reviews, 16(7), 4465-4469.
  • Song, Y., ve Wu, R. (2022). The impact of financial enterprises’ excessive financialization risk assessment for risk control based on data mining and machine learning. Computational Economics, 60(4), 1245-1267.
  • Şenol, Z., ve Zeren, F. (2020). Coronavirus (COVID-19) and stock markets: The effects of the pandemic on the global economy. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 7(4), 1-16.
  • Tahir, M., Enam, R. N., ve Mustafa, S. M. N. (2021, November). E-commerce platform based on Machine Learning Recommendation System. In 2021 6th International Multi-Topic ICT Conference (IMTIC) (pp. 1-4). IEEE.
  • Tang, Y., Yang, D., Li, W., Roth, H. R., Landman, B., Xu, D., ... ve Hatamizadeh, A. (2022). Self-supervised pre-training of swin transformers for 3d medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20730-20740).
  • Tay, L., Woo, S. E., Hickman, L., ve Saef, R. M. (2020). Psychometric and validity issues in machine learning approaches to personality assessment: A focus on social media text mining. European Journal of Personality, 34(5), 826-844.
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There are 63 citations in total.

Details

Primary Language Turkish
Subjects Information Systems (Other)
Journal Section Research Article
Authors

Fatih Sağlam 0000-0002-2084-2008

Mehmet Ali Cengiz

Publication Date October 18, 2023
Published in Issue Year 2023 Volume: 10 Issue: 4

Cite

APA Sağlam, F., & Cengiz, M. A. (2023). SOSYAL BİLİMLERDE VERİ BİLİMİ KULLANIMI. Avrasya Sosyal Ve Ekonomi Araştırmaları Dergisi, 10(4), 1175-1207.
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