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The Multi-Layered and Transformative Impact of Big Data, Artificial Intelligence, and the Internet of Behaviors on the Social Sciences

Year 2025, Volume: 5 Issue: 2, 117 - 124, 23.12.2025

Abstract

This study examines the multi-layered and transformative impact of big data, artificial intelligence (AI), and the Internet of Behaviors (IoB) on the social sciences. It argues that these technologies represent not merely new methodological tools, but a fundamental paradigm shift that challenges the field's core epistemological assumptions, ethical norms, and societal role. The analysis progresses through four key areas. First, it addresses the epistemological rupture, focusing on the "End of Theory" debate and the tension between correlation and causality in data-driven science. Second, it evaluates the methodological transformation, contrasting new research opportunities like predictive modeling and social simulation with significant risks, including algorithmic bias and the 'black-box' problem. Third, the study explores the profound ethical and political dimensions, such as surveillance, algorithmic authority, and power asymmetries. Finally, it analyzes the transition from the Internet of Things (IoT) to IoB, highlighting the shift from passive observation to active behavioral intervention, which poses significant threats to individual autonomy. The paper concludes that social scientists must adopt a critical role, moving beyond technical application to question the underlying power structures of these technologies and guide the development of a more just and democratic digital future.

References

  • Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
  • Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
  • Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
  • Ashton, K. (2009). That ‘Internet of Things’ thing. RFID Journal, 22(7), 97–114.
  • Gartner. (2020). Top strategic technology trends for 2021: Internet of behaviors. Gartner Research.
  • Audi, R. (2010). Epistemology: A contemporary introduction to the theory of knowledge (3rd ed.). Routledge.
  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
  • Frické, M. (2015). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651–661. https://doi.org/10.1002/asi.23212
  • Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/
  • Han, B.-C. (2017). Psychopolitics: Neoliberalism and new technologies of power. Verso Books.
  • Leonelli, S. (2020). Philosophy of data: Why data science is not a replacement for the scientific method. Harvard Data Science Review, 2(1). https://doi.org/10.1162/99608f92.74f2d3f6
  • Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885–899. https://doi.org/10.1177/0038038507080443
  • Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., … Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
  • Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
  • Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. ICWSM, 10(1), 178–185.
  • Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231. https://doi.org/10.1214/ss/1009213726
  • Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. American Economic Review, 105(5), 491–495. https://doi.org/10.1257/aer.p20151023
  • Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
  • Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://doi.org/10.48550/arXiv.2108.07258
  • Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7). https://doi.org/10.5210/fm.v19i7.4901
  • Grimmer, J. (2015). We are all social scientists now: How big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(1), 80–83. https://doi.org/10.1017/S1049096514001784
  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1). https://doi.org/10.1177/2053951714528481
  • Lum, K., & Isaac, W. (2016). To predict and serve? Significance, 13(5), 14–19. https://doi.org/10.1111/j.1740-9713.2016.00960.x
  • Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 31–57. https://doi.org/10.1145/3236386.3241340
  • Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A practical guide. Springer.
  • Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://doi.org/10.48550/arXiv.1702.08608
  • Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679
  • Sharon, T. (2016). The Googlization of health research: From disruptive innovation to disruptive ethics. Personalized Medicine, 13(6), 563–574. https://doi.org/10.2217/pme-2016-0057
  • Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.2139/ssrn.2477899
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
  • Andrejevic, M. (2014). Surveillance and alienation in the online economy. Surveillance & Society, 12(3), 381–397. https://doi.org/10.24908/ss.v12i3.5113
  • Ferguson, A. G. (2017). The rise of big data policing: Surveillance, race, and the future of law enforcement. NYU Press.
  • Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118–136. https://doi.org/10.1080/1369118X.2016.1186713
  • Sunstein, C. R. (2016). The ethics of influence: Government in the age of behavioral science. Cambridge University Press.
  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
  • Isaak, J., & Hanna, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer, 51(8), 56–59. https://doi.org/10.1109/MC.2018.3191268

Büyük Veri, Yapay Zeka ve Davranışların İnterneti'nin Sosyal Bilimlere Çok Katmanlı ve Dönüştürücü Etkisi

Year 2025, Volume: 5 Issue: 2, 117 - 124, 23.12.2025

Abstract

Bu çalışma, büyük verinin, yapay zekanın (AI) ve Davranışların İnterneti'nin (IoB) sosyal bilimler üzerindeki çok katmanlı ve dönüştürücü etkisini incelemektedir. Bu teknolojilerin yalnızca yeni metodolojik araçları temsil etmediğini, aynı zamanda alanın temel epistemolojik varsayımlarına, etik normlarına ve toplumsal rolüne meydan okuyan temel bir paradigma değişimini temsil ettiğini ileri sürüyor. Analiz dört temel alanda ilerlemektedir. İlk olarak, "Teorinin Sonu" tartışmasına ve veriye dayalı bilimde korelasyon ile nedensellik arasındaki gerilime odaklanarak epistemolojik kopuşa değiniyor. İkinci olarak, tahmine dayalı modelleme ve sosyal simülasyon gibi yeni araştırma fırsatlarını algoritmik önyargı ve 'kara kutu' sorunu gibi önemli risklerle karşılaştırarak metodolojik dönüşümü değerlendirir. Üçüncüsü, çalışma gözetim, algoritmik otorite ve güç asimetrileri gibi derin etik ve politik boyutları araştırıyor. Son olarak, Nesnelerin İnterneti'nden (IoT) IoB'ye geçişi analiz ederek, pasif gözlemden bireysel özerkliğe önemli tehditler oluşturan aktif davranışsal müdahaleye geçişi vurguluyor. Makale, sosyal bilimcilerin, bu teknolojilerin altında yatan güç yapılarını sorgulamak ve daha adil ve demokratik bir dijital geleceğin gelişimine rehberlik etmek için teknik uygulamanın ötesine geçerek kritik bir rol üstlenmeleri gerektiği sonucuna varıyor.

References

  • Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
  • Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
  • Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
  • Ashton, K. (2009). That ‘Internet of Things’ thing. RFID Journal, 22(7), 97–114.
  • Gartner. (2020). Top strategic technology trends for 2021: Internet of behaviors. Gartner Research.
  • Audi, R. (2010). Epistemology: A contemporary introduction to the theory of knowledge (3rd ed.). Routledge.
  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
  • Frické, M. (2015). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651–661. https://doi.org/10.1002/asi.23212
  • Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/
  • Han, B.-C. (2017). Psychopolitics: Neoliberalism and new technologies of power. Verso Books.
  • Leonelli, S. (2020). Philosophy of data: Why data science is not a replacement for the scientific method. Harvard Data Science Review, 2(1). https://doi.org/10.1162/99608f92.74f2d3f6
  • Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885–899. https://doi.org/10.1177/0038038507080443
  • Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., … Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
  • Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
  • Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. ICWSM, 10(1), 178–185.
  • Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231. https://doi.org/10.1214/ss/1009213726
  • Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. American Economic Review, 105(5), 491–495. https://doi.org/10.1257/aer.p20151023
  • Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
  • Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://doi.org/10.48550/arXiv.2108.07258
  • Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7). https://doi.org/10.5210/fm.v19i7.4901
  • Grimmer, J. (2015). We are all social scientists now: How big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(1), 80–83. https://doi.org/10.1017/S1049096514001784
  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1). https://doi.org/10.1177/2053951714528481
  • Lum, K., & Isaac, W. (2016). To predict and serve? Significance, 13(5), 14–19. https://doi.org/10.1111/j.1740-9713.2016.00960.x
  • Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 31–57. https://doi.org/10.1145/3236386.3241340
  • Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A practical guide. Springer.
  • Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://doi.org/10.48550/arXiv.1702.08608
  • Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679
  • Sharon, T. (2016). The Googlization of health research: From disruptive innovation to disruptive ethics. Personalized Medicine, 13(6), 563–574. https://doi.org/10.2217/pme-2016-0057
  • Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.2139/ssrn.2477899
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
  • Andrejevic, M. (2014). Surveillance and alienation in the online economy. Surveillance & Society, 12(3), 381–397. https://doi.org/10.24908/ss.v12i3.5113
  • Ferguson, A. G. (2017). The rise of big data policing: Surveillance, race, and the future of law enforcement. NYU Press.
  • Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118–136. https://doi.org/10.1080/1369118X.2016.1186713
  • Sunstein, C. R. (2016). The ethics of influence: Government in the age of behavioral science. Cambridge University Press.
  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
  • Isaak, J., & Hanna, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer, 51(8), 56–59. https://doi.org/10.1109/MC.2018.3191268
There are 37 citations in total.

Details

Primary Language English
Subjects Artificial Life and Complex Adaptive Systems
Journal Section Research Article
Authors

Bekir Emiroğlu 0000-0002-3395-5722

Submission Date November 10, 2025
Acceptance Date December 19, 2025
Publication Date December 23, 2025
Published in Issue Year 2025 Volume: 5 Issue: 2

Cite

IEEE B. Emiroğlu, “The Multi-Layered and Transformative Impact of Big Data, Artificial Intelligence, and the Internet of Behaviors on the Social Sciences”, Journal of Artificial Intelligence and Data Science, vol. 5, no. 2, pp. 117–124, 2025.

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