From Innovation to Controversy: Bibliometric Analysis of Artificial Intelligence and Deepfake Studies on Web of Science

Öz

Artificial intelligence technologies have increasingly begun to influence every level of societal life in recent years. With both positive and negative aspects, artificial intelligence is among the most popular technological developments today. The deepfake technology, which emerged due to advancements in artificial intelligence, is also a focal point of criticism in many respects. This study examines the results of a search conducted using the Web of Science database with the keywords "artificial intelligence" and "deepfake." Initially, 262 academic works (including articles, conference papers, books, and book chapters) related to these keywords were identified. To narrow the scope of the study, only research articles and early access articles were selected, and the search was restricted to journals indexed in Web of Science's SSCI, SCI-EXPANDED, ESCI, and AHCI indices for quality assessment. As a result of these restrictions, a total of 183 research articles were obtained. The study aims to evaluate the content and academic contributions of these articles. Among the key findings from the 183 reviewed studies, it was noted that studies from Turkey are limited, and while "deepfake" and "artificial intelligence" are prominent among common keywords, topics such as data security and social media are less frequently used.

Anahtar Kelimeler

• Artificial Intelligence
• Deepfake
• Manipulation
• Web of Science
• Bibliometric Analysis

Yenilikten Tartışmaya: Yapay Zekâ ve Deepfake Çalışmalarının Web of Science Üzerinden Bibliyometrik Analizi

Öz

Yapay zekâ teknolojileri son yıllarda toplumsal yaşamın her kademesini etkilemeye başlamıştır. Olumlu ve olumsuz yönleriyle yapay zekâ günümüzün en popüler teknolojik gelişmeleri arasında yer almaktadır. Yapay zekâ teknolojilerinin gelişimine bağlı olarak ortaya çıkan deepfake teknolojisi de birçok bakımından eleştirilerin odak noktasında yer almaktadır. Bu çalışma, Web of Science veri tabanı kullanılarak "yapay zekâ" ve "deepfake" anahtar kelimeleri ile yapılan taramanın sonuçlarını incelemektedir. Başlangıçta, bu anahtar kavramlarla ilgili 262 akademik çalışma (makale, bildiri, kitap ve kitap bölümü) tespit edilmiştir. Çalışmanın kapsamını daraltmak amacıyla, yalnızca araştırma makaleleri ve erken görünümdeki makaleler seçilmiş ve kalite değerlendirmesi için Web of Science’a özgü dergi indeksleri olan SSCI, SCI-EXPANDED, ESCI ve AHCI'de taranan dergilerle sınırlandırılmıştır. Bu sınırlamalar sonucunda, toplamda 183 araştırma makalesi elde edilmiştir. Çalışma, bu makalelerin içeriğini ve akademik katkılarını değerlendirmeyi hedeflemektedir. İncelenen 183 çalışmanın en önemli sonuçları arasında, Türkiye’deki çalışmaların kısıtlı olduğu, ortak anahtar kelimeler arasında "deepfake" ve "yapay zekâ" öne çıkarken, veri güvenliği ve sosyal medya gibi konular daha az kullanıldığı belirlenmiştir.

Anahtar Kelimeler

• Yapay Zekâ
• Deepfake
• Manipülasyon
• Web of Science
• Bibliyometrik Analiz

Kaynakça

1. Alsharif, A. H., & Baharun, R. (2020). Research trends of neuromarketıng: A bibliometric analysis. Journal of Theoretical and Applied Information Technology, 15, 15. www.jatit.org
2. Battista, D. (2024). Political communication in the age of artificial intelligence: An overview of deepfakes and their implications. Society Register, 8(2), 7-24.
3. Birer, C. G. (2020). Yapay Zekâ. Bilim ve Teknik, 630, 2-12.
4. Bukar, U. A., Sayeed, M. S., Razak, S. F. A., Yogarayan, S., Amodu, O. A., & Mahmood, R. A. R. (2023). A method for analyzing text using VOSviewer. MethodsX, 11, 102339. https://doi.org/10.1016/J.MEX.2023.102339
5. Castro, D., & New, J. (2016). The promise of artificial intelligence. Center for data innovation, 115(10).
6. Chadha, A., Kumar, V., Kashyap, S., & Gupta, M. (2021). Deepfake: An overview. Lecture Notes in Networks and Systems, 203 LNNS, 557-566. https://doi.org/10.1007/978-981- 16-0733-2_39
7. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146-166. https://doi.org/10.1016/J.JOI.2010.10.002
8. Demir, Y., & Öztürk, M. (2023). Tarihsel süreçte “Çevrimiçi Taciz”: Bibliyometrik bir analiz. Abant Sosyal Bilimler Dergisi, 23(2), 939-953. https://doi.org/10.11616/ASBI.1265610 Akdeniz İletişim | 2024 (46) | 73-93

9. Dickerman, L. (2000). Camera obscura: Socialist realism in the shadow of photography. October, 93, 138. https://doi.org/10.2307/779160
10. Dixit, A., Kaur, N., & Kingra, S. (2023). Review of audio deepfake detection techniques: Issues and prospects. Expert Systems, 40(8), e13322. https://doi.org/10.1111/EXSY.13322
11. Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., & Ferrer, C. C. (2020). The deepFake detection challenge (DFDC) dataset. arXiv preprint arXiv:2006.07397. https://arxiv.org/abs/2006.07397v4
12. Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809-1831. https://doi.org/10.1007/S11192-015-1645-Z/Tables/9
13. Fagni, T., Falchi, F., Gambini, M., Martella, A., & Tesconi, M. (2021). TweepFake: About detecting deepfake tweets. PLOS ONE, 16(5), e0251415. https://doi.org/10.1371/JOURNAL.PONE.0251415
14. Fetzer, J. H. (1990). What is artificial intelligence? Içinde Artificial intelligence: Its scope and limits.Studies in Cognitive Systems (C. 4, ss. 3-27). Springer, Dordrecht. https://doi.org/10.1007/978-94-009-1900-6_1
15. Fridman, M., Krøvel, R., & Palumbo, F. (2023). How (not to) run an AI project in investigative journalism. Journalism Practice. https://doi.org/10.1080/17512786.2023.2253797
16. Gaviria-Marin, M., Merigo, J. M., & Popa, S. (2018). Twenty years of the Journal of Knowledge Management: a bibliometric analysis. Journal of Knowledge Management, 22(8), 1655-1687. https://doi.org/10.1108/JKM-10-2017-0497/FULL/XML
17. Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence? Discover Artificial Intelligence 2022 2:1, 2(1), 1-19. https://doi.org/10.1007/S44163-022-00022-8
18. Karnouskos, S. (2020). Artificial intelligence in digital media: The era of deepfakes. IEEE Transactions on Technology and Society, 1(3), 138-147. https://doi.org/10.1109/TTS.2020.3001312
19. Langguth, J., Pogorelov, K., Brenner, S., Filkuková, P., & Schroeder, D. T. (2021). Don’t trust your eyes: Image manipulation in the age of deepFakes. Frontiers in Communication, 6, 632317. https://doi.org/10.3389/FCOMM.2021.632317/BIBTEX
20. Maras, M. H., & Alexandrou, A. (2018). Determining authenticity of video evidence in the age of artificial intelligence and in the wake of Deepfake videos. The International Journal of Evidence & Proof, 23(3), 255-262. https://doi.org/10.1177/1365712718807226
21. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on Artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12-12. https://doi.org/10.1609/AIMAG.V27I4.1904
22. Merigó, J. M., & Yang, J. B. (2017). A bibliometric analysis of operations research and management science. Omega, 73, 37-48. https://doi.org/10.1016/J.OMEGA.2016.12.004
23. Öztürk, M., & Demir, Y. (2023). Bilgilendirme ve kaos arasında: Afet yönetiminde medyanın rolüne yönelik bibliyometrik bir analiz. TRT Akademi, 8(18), 506-527. https://doi.org/10.37679/TRTA.1270615
24. Pawelec, M. (2022). Deepfakes and democracy (Theory): How synthetic audio-visual media for disinformation and hate speech threaten core democratic functions. Digital Society, 1(2), 1-37. https://doi.org/10.1007/S44206-022-00010-6
25. Preeti, Kumar, M., & Sharma, H. K. (2023). A GAN-Based model of deepfake detection in social media. Procedia Computer Science, 218, 2153-2162. https://doi.org/10.1016/J.PROCS.2023.01.191
26. Rana, M. S., Nobi, M. N., Murali, B., & Sung, A. H. (2022). Deepfake detection: A systematic literature review. IEEE Access, 10, 25494-25513. https://doi.org/10.1109/ACCESS.2022.3154404
27. Rani, R., Kumar, T., & Sah, M. P. (2022). A review on deepfake media detection. Lecture Notes in Networks and Systems, 461, 343-356. https://doi.org/10.1007/978-981-19- 2130-8_28
28. Rouhiainen, Lasse. (2019). Artificial intelligence: 101 things you must know today about our future (C. Estra, Ed.). Lasse Rouhiainen.
29. Sharma, V. K., Garg, R., & Caudron, Q. (2024). A systematic literature review on deepfake detection techniques. Multimedia Tools and Applications 2024, 1-43. https://doi.org/10.1007/S11042-024-19906-1
30. Valérie, D., & Pierre, A. G. (2010). Bibliometric indicators: Quality measurements of scientific publication 1. Radiological Society of North America, 255(2), 342-351. https://doi.org/10.1148/RADIOL.09090626
31. van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053-1070. https://doi.org/10.1007/S11192-017-2300-7/TABLES/4
32. Webster’s New World Dictionary. (2005). Webster’s II new college dictionary. Houghton Mifflin.
33. Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology Innovation Management Review, 9, 40-53. https://doi.org/http://doi.org/10.22215/timreview/1282
34. Zhang, T. (2022). Deepfake generation and detection, a survey. Multimedia Tools and Applications, 81(5), 6259-6276. https://doi.org/10.1007/S11042-021-11733-Y/METRICS