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FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES

Year 2025, Volume: 9 Issue: 2, 93 - 118, 18.07.2025

Cansu Aksu

https://doi.org/10.54707/meric.1571990

Abstract

This study aims to examine the impact of big data and digital transformation on research methods and applications in the social sciences. Big data surpasses traditional data collection and analysis processes, enabling social scientists to work with larger and more diverse datasets. In this context, the study thoroughly explores big data analytics methods and their application in the social sciences. Furthermore, it addresses how data collection and analysis processes have been restructured through digital transformation, as well as the opportunities and ethical challenges presented by big data. The findings suggest that big data and digital transformation contribute to making research processes in the social sciences more dynamic, comprehensive, and interdisciplinary. In conclusion, big data and digital transformation offer both new research opportunities and significant ethical and methodological challenges for the social sciences.

Keywords

Big data digital transformation policy-making ethics

References

  • Andrejevic, M. (2014). Big data, big questions| the big data divide. International Journal of Communication, 8, 17.
  • Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. Proceedings of the 21st International Conference on World Wide Web, 519–528.
  • Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, Issue 4). Springer. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.
  • Bovet, A., & Makse, H. A. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1), 7.
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
  • Bruns, A., Harrington, S., & Hurcombe, E. (2020). Covid19? Corona? 5G? or both?’: the dynamics of COVID-19/5G conspiracy theories on Facebook. Media International Australia, 177(1), 12–29.
  • Bruns, A., & Weller, K. (2016). Twitter as a first draft of the present: And the challenges of preserving it for the future. Proceedings of the 8th ACM Conference on Web Science, 183–189.
  • Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2–9.
  • Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10(1), 16598.
  • Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. In The costs of connection. Stanford University Press.
  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186.
  • Dignum, V. (2019). Responsible artificial intelligence: how to develop and use AI in a responsible way (Vol. 2156). Springer.
  • Einav, L., & Levin, J. (2014). Economics in the age of big data. Science, 346(6210), 1243089. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37.
  • Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230–235.
  • Few, S. (2009). Now you see it: simple visualization techniques for quantitative analysis. Analytics Press. Freeman, L. (2004). The development of social network analysis. A Study in the Sociology of Science, 1(687), 159–167.
  • George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. In Academy of management Journal (Vol. 57, Issue 2, pp. 321–326). Academy of Management Briarcliff Manor, NY.
  • Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, Issue 2). MIT press Cambridge. Gough, D., Thomas, J., & Oliver, S. (2017). An introduction to systematic reviews.
  • Greenhalgh, T., Thorne, S., & Malterud, K. (2018). Time to challenge the spurious hierarchy of systematic over narrative reviews? European Journal of Clinical Investigation, 48(6).
  • Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18, 43–49.
  • Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and. Techniques, Waltham: Morgan Kaufmann Publishers.
  • Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11(6), 838–854.
  • Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Communications of the ACM, 53(6), 59–67.
  • Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Computing Surveys (CSUR), 47(4), 1–38.
  • Jurafsky, D., & Martin, J. H. (2020). Speech and Language Processing. Draft of 3rd edition. Kirk, A. (2019). Data visualisation: A handbook for data driven design.
  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage. Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788–8790. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science, 343(6176), 1203–1205.
  • Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., & Margetts, H. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062.
  • Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., & Gutmann, M. (2009). Computational social science. Science, 323(5915), 721–723.
  • Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 55–60.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
  • Mejias, U. A., & Couldry, N. (2024). Data grab: The new colonialism of big tech and how to fight back. In Data Grab. University of Chicago Press.
  • Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. LREc, 10(2010), 1320–1326.
  • Pósfai, M., & Barabási, A.-L. (2016). Network science (Vol. 3). Citeseer.
  • Taylor, L., Schroeder, R., & Meyer, E. (2014). Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same? Big Data & Society, 1(2), 2053951714536877.
  • Tene, O., & Polonetsky, J. (2012). Big data for all: Privacy and user control in the age of analytics. Nw. J. Tech. & Intell. Prop., 11, 239.
  • Tisné, M., & Schaake, M. (2020). The data delusion: Protecting individual data isn’t enough when the harm is collective. Luminate, July.
  • Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 505–514.
  • Tufte, E. R. (2001). The visual display of quantitative information. 2nd. Graphics Pr, May. Vlačić, B., Corbo, L., e Silva, S. C., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187–203.
  • Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. In Journal of Business logistics (Vol. 34, Issue 2, pp. 77–84). Wiley Online Library.
  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.
  • Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005). Practical machine learning tools and techniques. Data Mining, 2(4), 403–413.
  • Ziewitz, M. (2016). Governing algorithms: Myth, mess, and methods. Science, Technology, & Human Values, 41(1), 3–16.
  • Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., & Tolmie, P. (2016). Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS One, 11(3), e0150989.
  • Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), 2053951714559253.

BÜYÜK VERİ VE SAYISAL DÖNÜŞÜMÜN SOSYAL BİLİMLERDEKİ ROLÜ

Year 2025, Volume: 9 Issue: 2, 93 - 118, 18.07.2025

Cansu Aksu

https://doi.org/10.54707/meric.1571990

Abstract

Bu çalışma, büyük veri ve sayısal dönüşümün sosyal bilimlerdeki araştırma yöntemleri ve uygulamaları üzerindeki etkilerini incelemeyi amaçlamaktadır. Büyük veri, geleneksel veri toplama ve analiz süreçlerinin ötesine geçerek, sosyal bilimcilerin daha geniş ve çeşitli veri kümeleri üzerinde çalışmasına olanak sağlamaktadır. Bu bağlamda, çalışmada büyük veri analitiği yöntemleri ve bu yöntemlerin sosyal bilimlerdeki kullanımı detaylı bir şekilde ele alınmıştır. Ayrıca, dijital dönüşümle birlikte veri toplama ve analiz süreçlerinin nasıl yeniden yapılandırıldığı, büyük veri ile ortaya çıkan fırsatlar ve bu süreçlerin beraberinde getirdiği etik sorunlar da tartışılmaktadır. Bulgular, büyük veri ve dijital dönüşümün, sosyal bilimlerdeki araştırma süreçlerini daha dinamik, kapsamlı ve disiplinler arası bir yapıya dönüştürdüğünü ortaya koymaktadır. Sonuç olarak, büyük veri ve dijital dönüşüm, sosyal bilimler için hem yeni araştırma olanakları hem de önemli etik ve metodolojik zorluklar sunmaktadır.

Keywords

Büyük veri sayısal dönüşüm politika yapımı etik

References

  • Andrejevic, M. (2014). Big data, big questions| the big data divide. International Journal of Communication, 8, 17.
  • Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. Proceedings of the 21st International Conference on World Wide Web, 519–528.
  • Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, Issue 4). Springer. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.
  • Bovet, A., & Makse, H. A. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1), 7.
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
  • Bruns, A., Harrington, S., & Hurcombe, E. (2020). Covid19? Corona? 5G? or both?’: the dynamics of COVID-19/5G conspiracy theories on Facebook. Media International Australia, 177(1), 12–29.
  • Bruns, A., & Weller, K. (2016). Twitter as a first draft of the present: And the challenges of preserving it for the future. Proceedings of the 8th ACM Conference on Web Science, 183–189.
  • Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2–9.
  • Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10(1), 16598.
  • Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. In The costs of connection. Stanford University Press.
  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186.
  • Dignum, V. (2019). Responsible artificial intelligence: how to develop and use AI in a responsible way (Vol. 2156). Springer.
  • Einav, L., & Levin, J. (2014). Economics in the age of big data. Science, 346(6210), 1243089. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37.
  • Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230–235.
  • Few, S. (2009). Now you see it: simple visualization techniques for quantitative analysis. Analytics Press. Freeman, L. (2004). The development of social network analysis. A Study in the Sociology of Science, 1(687), 159–167.
  • George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. In Academy of management Journal (Vol. 57, Issue 2, pp. 321–326). Academy of Management Briarcliff Manor, NY.
  • Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, Issue 2). MIT press Cambridge. Gough, D., Thomas, J., & Oliver, S. (2017). An introduction to systematic reviews.
  • Greenhalgh, T., Thorne, S., & Malterud, K. (2018). Time to challenge the spurious hierarchy of systematic over narrative reviews? European Journal of Clinical Investigation, 48(6).
  • Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18, 43–49.
  • Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and. Techniques, Waltham: Morgan Kaufmann Publishers.
  • Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11(6), 838–854.
  • Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Communications of the ACM, 53(6), 59–67.
  • Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Computing Surveys (CSUR), 47(4), 1–38.
  • Jurafsky, D., & Martin, J. H. (2020). Speech and Language Processing. Draft of 3rd edition. Kirk, A. (2019). Data visualisation: A handbook for data driven design.
  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage. Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788–8790. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science, 343(6176), 1203–1205.
  • Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., & Margetts, H. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062.
  • Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., & Gutmann, M. (2009). Computational social science. Science, 323(5915), 721–723.
  • Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 55–60.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
  • Mejias, U. A., & Couldry, N. (2024). Data grab: The new colonialism of big tech and how to fight back. In Data Grab. University of Chicago Press.
  • Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. LREc, 10(2010), 1320–1326.
  • Pósfai, M., & Barabási, A.-L. (2016). Network science (Vol. 3). Citeseer.
  • Taylor, L., Schroeder, R., & Meyer, E. (2014). Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same? Big Data & Society, 1(2), 2053951714536877.
  • Tene, O., & Polonetsky, J. (2012). Big data for all: Privacy and user control in the age of analytics. Nw. J. Tech. & Intell. Prop., 11, 239.
  • Tisné, M., & Schaake, M. (2020). The data delusion: Protecting individual data isn’t enough when the harm is collective. Luminate, July.
  • Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 505–514.
  • Tufte, E. R. (2001). The visual display of quantitative information. 2nd. Graphics Pr, May. Vlačić, B., Corbo, L., e Silva, S. C., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187–203.
  • Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. In Journal of Business logistics (Vol. 34, Issue 2, pp. 77–84). Wiley Online Library.
  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.
  • Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005). Practical machine learning tools and techniques. Data Mining, 2(4), 403–413.
  • Ziewitz, M. (2016). Governing algorithms: Myth, mess, and methods. Science, Technology, & Human Values, 41(1), 3–16.
  • Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., & Tolmie, P. (2016). Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS One, 11(3), e0150989.
  • Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), 2053951714559253.
There are 45 citations in total.

Details

Primary Language English
Subjects Policy and Administration (Other)
Journal Section Derleme Makalesi
Authors

Cansu Aksu 0000-0001-5717-2821

Publication Date July 18, 2025
Submission Date October 22, 2024
Acceptance Date June 11, 2025
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

APA Aksu, C. (2025). FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES. Meriç Uluslararası Sosyal Ve Stratejik Araştırmalar Dergisi, 9(2), 93-118. https://doi.org/10.54707/meric.1571990
AMA Aksu C. FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES. Meriç Uluslararası Sosyal ve Stratejik Araştırmalar Dergisi. July 2025;9(2):93-118. doi:10.54707/meric.1571990
Chicago Aksu, Cansu. “FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES”. Meriç Uluslararası Sosyal Ve Stratejik Araştırmalar Dergisi 9, no. 2 (July 2025): 93-118. https://doi.org/10.54707/meric.1571990.
EndNote Aksu C (July 1, 2025) FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES. Meriç Uluslararası Sosyal ve Stratejik Araştırmalar Dergisi 9 2 93–118.
IEEE C. Aksu, “FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES”, Meriç Uluslararası Sosyal ve Stratejik Araştırmalar Dergisi, vol. 9, no. 2, pp. 93–118, 2025, doi: 10.54707/meric.1571990.
ISNAD Aksu, Cansu. “FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES”. Meriç Uluslararası Sosyal ve Stratejik Araştırmalar Dergisi 9/2 (July 2025), 93-118. https://doi.org/10.54707/meric.1571990.
JAMA Aksu C. FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES. Meriç Uluslararası Sosyal ve Stratejik Araştırmalar Dergisi. 2025;9:93–118.
MLA Aksu, Cansu. “FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES”. Meriç Uluslararası Sosyal Ve Stratejik Araştırmalar Dergisi, vol. 9, no. 2, 2025, pp. 93-118, doi:10.54707/meric.1571990.
Vancouver Aksu C. FUNCTIONAL APPLICATIONS OF BIG DATA AND DIGITAL TRANSFORMATION IN SOCIAL SCIENCES. Meriç Uluslararası Sosyal ve Stratejik Araştırmalar Dergisi. 2025;9(2):93-118.
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