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Journals / Intermedia International e-Journal / 2026 / Cilt: 12 Sayı: 22

Deepfake Manipulation on Social Media: A Case Study of Fraudulent Activities in Türkiye

Sosyal Medyada Deepfake Manipülasyonu: Türkiye'deki Dolandırıcılık Faaliyetleri Üzerine Bir Vaka Çalışması

Türker Söğütlüler(AYDIN ADNAN MENDERES ÜNİVERSİTESİ)0000-0003-1154-1112
Pages
237–265
Publication Date
2025-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It was found that the videos included important political figures, scientists, artists, journalists-anchors, business people and all of them were fraudulent.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The term "deepfake," derived from the words "deep" and "fake," refers to highly realistic and deceptive content designed to manipulate perception.
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Key Findings & Data:Deepfake content based on artificial intelligence technology poses a threat when integrated with the possibilities of digital culture, and illegal activities are carried out by changing the faces and speeches of famous people.
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Conclusion & Impact:It was found that the videos included important political figures, scientists, artists, journalists-anchors, business people and all of them were fraudulent.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zeka · Deepfake · Dijital Kültür

Abstract

The term "deepfake," derived from the words "deep" and "fake," refers to highly realistic and deceptive content designed to manipulate perception. Deepfake content based on artificial intelligence technology poses a threat when integrated with the possibilities of digital culture, and illegal activities are carried out by changing the faces and speeches of famous people. The study was conducted to examine social media-mediated deepfake fraud in Turkey. Facebook and Instagram platforms were scanned with four different accounts for a period of sixteen months and fifty-six deepfake videos were obtained. It was found that the videos included important political figures, scientists, artists, journalists-anchors, business people and all of them were fraudulent. It was observed that Lip-Sync (m = 0.75) was the primary model in the production of the videos, followed by Text-to-speech (m = 0.18), Lip-Sync & Text-to-speech (m = 0.5). The group with the most deepfake videos was business people (m = 0.35), followed by political figures (m = 0.33), scientists (m = 0.14), journalists-speakers (m = 0.12), artists (m = 0.03). The deepfake videos of Ali Koç, Ekrem İmamoğlu, Emine Erdoğan, Hakan Fidan and Murat Ülker, which were selected based on Lip-Sync performance, were analysed with Deepware and the detection architecture was found to be insufficient. In the research, which uses thematic analysis method and quantitative data in a mixed format, frequency distributions were created with IBM SPSS Statistics 22.0 programme. In the light of the findings, suggestions for the prevention of victimisation are presented.

Özet

“Derin” ve “sahte” kelimelerinin birleşimiyle oluşan deepfake kavramı manipülatif, hiper-gerçekçi içerikleri tanımlamak için kullanılmaktadır. Yapay zeka teknolojisine dayanan deepfake içerikler, dijital kültürün olanakları ile bütünleştiğinde tehdit oluşturmakta, ünlü kişilerin yüzleri-konuşmaları değiştirilerek yasa dışı faaliyetler yürütülmektedir. Çalışma; Türkiye’de sosyal medya aracılı deepfake dolandırıcılığını incelemek amacıyla gerçekleştirilmiştir. Facebook ve Instagram platformları on altı aylık bir süre boyunca dört farklı hesapla taranmış, elli altı deepfake video elde edilmiştir. Elde edilen videoların Türkiye’nin önemli siyasi figürleri, bilim insanları, sanatçıları, gazetecileri-spikerleri, iş insanlarını içerdiği ve tümünün dolandırıcılık faaliyeti içerdiği saptanmıştır. Videoların üretiminde öncelikli modelin Lip-Sync (m = 0.75) olduğu, bu modeli Text-to-speech (m = 0.18), Lip-Sync & Text-to-speech (m = 0.5) modellerinin takip ettiği görülmüştür. En çok deepfake videoya sahip grup iş insanları (m = 0.35) olmuş, bu grubu siyasi figürler (m = 0.33), bilim insanları (m = 0.14), gazeteciler-spikerler (m = 0.12), sanatçılar (m = 0.03) takip etmiştir. Lip-Sync performansına dayanarak seçilen; Ali Koç, Ekrem İmamoğlu, Emine Erdoğan, Hakan Fidan ve Murat Ülker’in deepfake videoları Deepware ile analiz edilmiş, tespit mimarisinin yetersiz kaldığı görülmüştür. Tematik analiz yöntemini ve nicel verileri karma biçimde kullanan araştırmada, frekans dağılımları IBM SPSS Statistics 22.0 programıyla oluşturulmuştur. Elde edilen bulgular ışığında, mağduriyetlerin önlenmesine yönelik öneriler sunulmuştur.

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Cite This Article

Söğütlüler, T. (2025). Deepfake Manipulation on Social Media: A Case Study of Fraudulent Activities in Türkiye. Intermedia International e-Journal, Cilt: 12 Sayı: 22, 237–265. https://doi.org/10.56133/intermedia.1614843

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References (73)

  1. Ab-Khazraji, S. H., Saleh, H. H., Khalıd, A. I., & Mıshkhal, I. A. (2023). Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 23, 429-441. https://doi.org/10.55549/epstem.1371792
  2. Acar, H. M., & İmik Tanyıldızı, N. (2022). Reklamda Yapay Zeka Kullanımı: Ziraat Bankası #Senhepgülümse Reklam Filminde Deepfake Uygulamasının Görsel Anlatıya Etkisi. Kastamonu İletişim Araştırmaları Dergisi(8), 78-99.
  3. Agarwal, S., & Farid, H. (2021). Detecting deep-fake videos from aural and oral dynamics. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 981-989). https://doi.org/10.1109/CVPRW53098.2021.00109
  4. Aggarwal, A., Mittal, M., & Battineni, G. (2021). Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, 1(1), 100004. https://doi.org/10.1016/j.procs.2024.04.090
  5. Ahmed, S. (2021). Who inadvertently shares deepfakes? Analyzing the role of political interest, cognitive ability, and social network size. Telematics and Informatics, 57, 101508. https://doi.org/10.1016/j.tele.2020.101508
  6. Ahmed, S. R., Sonuç, E., Ahmed, M. R., & Duru, A. D. (2022). Analysis survey on deepfake detection and recognition with convolutional neural networks. 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 1–7. https://doi.org/10.1109/HORA55278.2022.9799858
  7. Allen, C., Payne, B. R., Abegaz, T., & Robertson, C. (2024). What You See Is Not What You Know: Studying Deception in Deepfake Video Manipulation. Journal of Cybersecurity Education, Research and Practice, 2024(1). 1-7. https://doi.org/10.32727/8.2023.25
  8. Berk, M. E. (2020). Dijital çağın yeni tehlikesi “deepfake”. OPUS International Journal of Society Researches, 16(28), 1508-1523. https://doi.org/10.26466/opus.683819
  9. Bondi, L., Cannas, E. D., Bestagini, P., & Tubaro, S. (2020). Training strategies and data augmentations in CNN-based DeepFake video detection. 2020 IEEE International Workshop on Information Forensics and Security (WIFS), 1–6. https://doi.org/10.1109/WIFS49906.2020.9360901
  10. Chadha, A., Kumar, V., Kashyap, S., Gupta, M. (2021). Deepfake: An Overview. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (eds) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security.
  11. Lecture Notes in Networks and Systems, vol 203. Springer, Singapore. https://doi.org/10.1007/978-981-16-0733-2_39
  12. Chen, D., Chen, Q., Wu, J., Yu, X., & Jia, T. (2019). Face swapping: realistic image synthesis based on facial landmarks alignment. Mathematical Problems in Engineering, 2019(1), 8902701. https://doi.org/10.1155/2019/8902701
  13. Chesney, R., & Citron, D. K. (2019a). Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security. California Law Review, 107, 1753.
  14. Chesney, D. & Citron, R. (2019b). Deepfakes and the new disinformation war: The coming age of post-truth geopolitics. Foreign Affairs 98(1). (pp. 147–153).
  15. Citron, D. (2018) Deepfakes and the New Disinformation War https://cyberlaw.stanford.edu/publications/deepfakes-and-new-disinformation-war/
  16. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE signal processing magazine, 35(1), 53-65. https://doi.org/10.1109/MSP.2017.2765202
  17. Çil, S. (2023). Türkiye’de Deepfake Teknolojisi: Youtube’da En Çok İzlenen Türkçe Deepfake Videolar Ve İzleyicileri Üzerine İnceleme. Uluslararası İletişim ve Sanat Dergisi, 4(Özel Sayı), 173-195.
  18. Dennett, D. (2009). Preface. In R. Epstein, G. Roberts, & G. Beber (Eds.), Parsing the Turing Test (pp. xv-xvii). Springer. https://doi.org/10.1007/978-1-4020-6710-5
  19. Deepware. (n.d.). Deepware Scanner. Retrieved December 21, 2024, from https://scanner.deepware.ai/
  20. Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Canton Ferrer, C. (2019). The Deepfake Detection Challenge (DFDC) preview dataset. arXiv. https://doi.org/10.48550/arXiv.1910.08854
  21. Epstein, R. (2009). Introduction. In R. Epstein, G. Roberts, & G. Beber (Eds.), Parsing the Turing Test (pp. 1-10). Springer. https://doi.org/10.1007/978-1-4020-6710-5
  22. Fetzer, J.H. (1990). What is Artificial Intelligence?. In: Artificial Intelligence: Its Scope and Limits. Studies in Cognitive Systems, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-1900-6_1
  23. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press
  24. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S.,. & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
  25. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37), eaay7120. https://doi.org/doi:10.1126/scirobotics.aay7120
  26. Gupta, P., Chugh, K., Dhall, A., & Subramanian, R. (2020). The eyes know it: Fakeet-an eye-tracking database to understand deepfake perception. In Proceedings of the 2020 international conference on multimodal interaction (pp. 519-527). https://doi.org/10.1145/3382507.3418857
  27. Güera, D., & Delp, E. J. (2018, November). Deepfake video detection using recurrent neural networks. In 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS) (pp. 1-6). IEEE. https://doi.org/10.1109/AVSS.2018.8639163
  28. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N.,. & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), 82-97. https://doi.org/10.1109/MSP.2012.2205597
  29. Ho, V. (2024) Fighting deepfakes with more transparency about AI https://news.microsoft.com/source/features/ai/fighting-deepfakes-with-more-transparency-about-ai/
  30. Intel (2022) Intel introduces real-time deepfake detector. https://www.intel.com/content/www/us/en/newsroom/news/intel-introduces-real-time-deepfake-detector.html#gs.ixyrhf
  31. Jung, T., Kim, S., & Kim, K. (2020). DeepVision: Deepfakes detection using human eye blinking pattern. IEEE Access, 8, 83144–83154. https://doi.org/10.1109/ACCESS.2020.2988660 Karnouskos, S. (2020). Artificial intelligence in digital media: The era of deepfakes. IEEE Transactions on Technology and Society, 1(3), 138-147.
  32. Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024). Deepfake video detection: challenges and opportunities. Artificial Intelligence Review, 57(6), 1-47. https://doi.org/10.1007/s10462-024-10810-6
  33. Karakoç, E., & Zeybek, B. (2022). Görmek inanmaya yeter mi? görsel dezenformasyonun ayırt edici biçimi olarak siyasi deepfake içerikler. Öneri Dergisi, 17(57), 50-72. https://doi.org/10.14783/maruoneri.908542
  34. Karcı, H. D. (2024). Reklamcılıkta Deepfake Ugulamasına Neil Postman’ın Beş Fikirli Perspektifiyle Yaklaşmak: Durum Çalışması. Social Mentality And Researcher Thinkers Journal (Smart Journal), 9(68), 2864-2886. https://doi.org/10.29228/smryj.68267
  35. Kırık, A. M., & Özkoçak, V. (2023). Medya ve İletişim Bağlamında Yapay Zekâ Tarihi ve Teknolojisi: Chatgpt ve Deepfake ile Gelen Dijital Dönüşüm. Karadeniz Uluslararası Bilimsel Dergi, (58), 73-99. https://doi.org/10.17498/kdeniz.1308471
  36. Koopman, M., Rodriguez, A. M., & Geradts, Z. (2018, August). Detection of deepfake video manipulation. In The 20th Irish machine vision and image processing conference (IMVIP) (pp. 133-136).
  37. Korshunov P, Marcel S (2018) Speaker inconsistency detection in tampered video. In: 2018 26th european signal processing conference (EUSIPCO). IEEE https://doi.org/10.23919/EUSIPCO.2018.8553270
  38. Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage. https://doi.org/10.4135/9781071878781 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
  39. Kwok, A. O. J., & Koh, S. G. M. (2020). Deepfake: A social construction of technology perspective. Current Issues in Tourism. Advance online publication. https://doi.org/10.1080/13683500.2020.1738357
  40. Letheren, K., Russell-Bennett, R., & Whittaker, L. (2020). Black, white or grey magic? Our future with artificial intelligence. Journal of Marketing Management, 36(3-4), 216-232. https://doi.org/10.1080/0267257X.2019.1706306
  41. Li Y, Chang M-C, Lyu S (2018) In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In: 2018 IEEE international workshop on information forensics and security (WIFS). IEEE. https://doi.org/10.1109/WIFS.2018.8630787
  42. Lucas, K. T. (2022). Deepfakes and Domestic Violence: Perpetrating Intimate Partner Abuse Using Video Technology. Victims & Offenders, 17(5), 647–659. https://doi.org/10.1080/15564886.2022.2036656
  43. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), 381-386. https://doi.org/10.21275/ART20203995
  44. Maras, M.-H., & Alexandrou, A. (2019). 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
  45. Mondal, B. (2020). Artificial Intelligence: State of the Art. In: Balas, V., Kumar, R., Srivastava, R. (eds) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-030-32644-9_32
  46. Montserrat, D. M., Hao, H., Yarlagadda, S. K., Baireddy, S., Shao, R., Horváth, J., Bartusiak, E., Yang, J., Güera, D., Zhu, F., & Delp, E. J. (2020). Deepfakes detection with automatic face weighting. arXiv. https://doi.org/10.48550/arXiv.2004.12027
  47. Murphy, G., & Flynn, E. (2022). Deepfake false memories. In Memory Online (pp. 112-124). Routledge.
  48. Neuendorf, K. A. (2018). Content analysis and thematic analysis. In Advanced research methods for applied psychology (pp. 211-223). Routledge.
  49. Naitali, A., Ridouani, M., Salahdine, F., & Kaabouch, N. (2023). Deepfake attacks: Generation, detection, datasets, challenges, and research directions. Computers, 12(10), 216. https://doi.org/10.3390/computers12100216
  50. Pan, S, J. Yang, Q. (2009) A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10):1345–1359. https://doi.org/10.1109/TKDE.2009.191
  51. Pashine, S., Mandiya, S., Gupta, P., & Sheikh, R. (2021). Deep fake detection: survey of facial manipulation detection solutions. arXiv preprint arXiv:2106.12605. https://doi.org/10.48550/arXiv.2106.12605
  52. Pei, G., Zhang, J., Hu, M., Zhang, Z., Wang, C., Wu, Y.,. & Tao, D. (2024). Deepfake generation and detection: A benchmark and survey. arXiv preprint arXiv:2403.17881. https://doi.org/10.48550/arXiv.23.17881
  53. Rana, J., Gaur, L., Singh, G., Awan, U., & Rasheed, M. I. (2022). Reinforcing customer journey through artificial intelligence: a review and research agenda. International Journal of Emerging Markets, 17(7), 1738-1758. https://doi.org/10.1108/IJOEM-08-2021-1214
  54. Raza, A., Munir, K., & Almutairi, M. (2022). A novel deep learning approach for deepfake image detection. Applied Sciences, 12(19), 9820. https://doi.org/10.3390/app12199820
  55. Shao, R., Wu, T., Liu, Z. (2022). Detecting and Recovering Sequential DeepFake Manipulation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13673. Springer, Cham. https://doi.org/10.1007/978-3-031-19778-9_41
  56. Söğütlüler, T. (2024). An applied research on the use of artificial intelligence technologies in moving image production. İNİF E- Dergi, 9(2), 1-26. https://doi.org/10.47107/inifedergi.1512175
  57. Sözcü. (2024, 17 Aralık). Asgari ücretli Birgül 650 bin lirayı yapay zeka Timur'a kaptırdı. Sözcü Gazetesi. https://www.sozcu.com.tr/asgari-ucretli-birgul-650-bin-lirayi-yapay-zeka-timur-a-kaptirdi-p116675
  58. Tahir, R., Batool, B., Jamshed, H., Jameel, M., Anwar, M., Ahmed, F.,. & Zaffar, M. F. (2021, May). Seeing is believing: Exploring perceptual differences in deepfake videos. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-16). https://doi.org/10.1145/3411764.3445699
  59. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020). Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion, 64, 131-148. https://doi.org/10.1016/j.inffus.2020.06.014
  60. Tulga, A. Y. (2024). A Comprehensive Analysis of Public Discourse and Content Trends in Turkish Reddit Posts Related to Deepfake. Journal of Global and Area Studies, 8(2), 257-276. https://doi.org/10.31720/JGA.8.2.13
  61. Turing, A., M. (1950). Computing machinery and intelligence, Mind 59(236): 433–460.
  62. Turing, A., M. (2009). Computing Machinery and Intelligence. In: Epstein, R., Roberts, G., Beber, G. (eds) Parsing the Turing Test. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6710-5_3
  63. Vurgun, Ş., & Akpınar, G. M. (2020). Blockchain technology and right to be forgotten. Journal of International Social Research, 13(74), 492-502. https://doi.org/10.17719/jisr.11235
  64. Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., & Wang, F. Y. (2017). Generative adversarial networks: introduction and outlook. IEEE/CAA Journal of Automatica Sinica, 4(4), 588-598. https://doi.org/10.1109/JAS.2017.7510583 Weber, R. (1990). Basic content analysis. Thousand Oaks, CA: Sage., https://doi.org/10.4135/9781412983488
  65. Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology innovation management review, 9(11). https://doi.org/10.22215/timreview/1282
  66. Yu, N., Davis, L., & Fritz, M. (2018). Attributing fake images to GANs: Learning and analyzing GAN fingerprints. arXiv. https://doi.org/10.48550/arXiv.1811.08180
  67. Yu, P., Xia, Z., Fei, J., & Lu, Y. (2021). A survey on deepfake video detection. Iet Biometrics, 10(6), 607-624. https://doi.org/10.1049/bme2.12031
  68. Zhang, T. Deepfake generation and detection, a survey. Multimed Tools Appl 81, 6259–6276 (2022). https://doi.org/10.1007/s11042-021-11733-y
  69. Zhao, Y., Ge, W., Li, W., Wang, R., Zhao, L., Ming, J. (2020). Capturing the Persistence of Facial Expression Features for Deepfake Video Detection. In: Zhou, J., Luo, X., Shen, Q., Xu, Z. (eds) Information and Communications Security. ICICS 2019. Lecture Notes in Computer Science(), vol 11999. Springer, Cham. https://doi.org/10.1007/978-3-030-41579-2_37
  70. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S.,. & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
  71. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N.,. & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), 82-97. https://doi.org/10.1109/MSP.2012.2205597
  72. Pei, G., Zhang, J., Hu, M., Zhang, Z., Wang, C., Wu, Y.,. & Tao, D. (2024). Deepfake generation and detection: A benchmark and survey. arXiv preprint arXiv:2403.17881. https://doi.org/10.48550/arXiv.23.17881
  73. Tahir, R., Batool, B., Jamshed, H., Jameel, M., Anwar, M., Ahmed, F.,. & Zaffar, M. F. (2021, May). Seeing is believing: Exploring perceptual differences in deepfake videos. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-16). https://doi.org/10.1145/3411764.3445699