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

Year 2025, Volume: 12 Issue: 22, 237 - 265, 30.06.2025

Türker Söğütlüler

https://doi.org/10.56133/intermedia.1614843

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.

Keywords

Artificial Intelligence Deepfake Digital Culture New Communication Technologies

Ethical Statement

Çalışma bilimsel etik kurallar çerçevesinde hazırlanmıştır.

Thanks

Editoryal süreçteki emekleriniz için çok teşekkür eder, çalışmalarınızda başarılar dilerim.

References

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

Year 2025, Volume: 12 Issue: 22, 237 - 265, 30.06.2025

Türker Söğütlüler

https://doi.org/10.56133/intermedia.1614843

Abstract

“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.

Keywords

Yapay Zeka Deepfake Dijital Kültür Yeni İletişim Teknolojileri

References

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  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
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  • 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
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  • 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).
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  • 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
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Details

Primary Language English
Subjects Communication Studies, Communication Technology and Digital Media Studies
Journal Section Articles
Authors

Türker Söğütlüler 0000-0003-1154-1112

Early Pub Date June 29, 2025
Publication Date June 30, 2025
Submission Date January 7, 2025
Acceptance Date March 2, 2025
Published in Issue Year 2025 Volume: 12 Issue: 22

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