Cited by 98Misinformation and Its ImpactsDigital Media Forensic DetectionSpam and Phishing Detection

Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications

Open access
AL-KHAZRAJI, Samer Hussain, SALEH, Hassan Hadi, KHALID, Adil Ibrahim, MISHKHAL, Israa Adnan·2023·The Eurasia Proceedings of Science Technology Engineering and Mathematics

Abstract

Deepfake technology, which allows the manipulation and fabrication of audio, video, and images, has gained significant attention due to its potential to deceive and manipulate. As deepfakes proliferate on social media platforms, understanding their impact becomes crucial. This research investigates the detection, misinformation, and societal implications of deepfake technology on social media. Through a comprehensive literature review, the study examines the development and capabilities of deepfakes, existing detection techniques, and challenges in identifying them. The role of deepfakes in spreading misinformation and disinformation is explored, highlighting their potential consequences on public trust and social cohesion. The societal implications and ethical considerations surrounding deepfakes are examined, along with legal and policy responses. Mitigation strategies, including technological advancements and platform policies, are discussed. By shedding light on these critical aspects, this research aims to contribute to a better understanding of the impact of deepfake technology on social media and to inform future efforts in detection, prevention, and policy development.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.55549/epstem.1371792

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Instant messaging systems such as WhatsApp, Telegram, Viber, and Signal have created new spaces for exchange and sharing where content, including deepfakes, can be shared and reshared virtually without control.

    “the success of instant messaging systems such as WhatsApp, Telegram, or (to a lesser extent) Viber and Signal has created new spaces for exchange and sharing, such as channels and groups, where all kinds of content, including deepfakes, can be shared and reshared virtually without control (Al-Khazraji et al., 2023).”

    From Deepfakes, Democracy, and the Ethics of Synthetic Media: A Synthesis of the SOLARIS Project (2026) · cites this paper as Al-Khazraji et al., 2023

    The 'Spread of Misinformation and Disinformation' section says deepfakes can be shared widely and quickly, making their dissemination difficult to contain. It does not identify WhatsApp, Telegram, Viber, Signal, channels, or groups as the spaces responsible; searches for the named services and instant messaging found no matching passage. The cited paper therefore supports the general point about spread, not the claim's messaging-specific explanation. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 39

  1. Ali, S., DiPaola, D., & Breazeal, C. (2021). What are GANs?: Introducing generative adversarial networks to middle school students. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15472- 15479).
  2. Borji, A. (2023). Qualitative failures of image generation models and their application in detecting deepfakes. arXiv, 1.
  3. Chintha, A., Thai, B., Sohrawardi, S.J., Bhatt, K., Hickerson, A., Wright, M., & Ptucha, R. (2020). Recurrent convolutional structures for audio spoof and video deepfake detection. IEEE Journal of Selected Topics in Signal Processing, 14(5), 1024-1037.
  4. Dang Nguyen, D.T., Pasquini, C., Conotter, V., & Boato, G. (2015). Raise: a raw images dataset for digital image forensics. In Proceedings of the 6th ACM multimedia systems conference, 219-224.
  5. de Rancourt-Raymond, A., & Smaili, N. (2023). The unethical use of deepfakes. Journal of Financial Crime, 30(4), 1066-1077.
  6. Gao, Y. (2022). Audio deepfake detection based on differences in human and machine generated peech. (Doctoral dissertation). Carnegie Mellon University.
  7. Geddes, K. (2020). Ocularcentrism and deepfakes: Should seeing be believing? Fordham Intellectual Property, Media & Entertainment Law Journal, 31, 1042.
  8. Giudice, O., Guarnera, L., & Battiato, S. (2021). Fighting deepfakes by detecting gan dct anomalies. Journal of Imaging, 7(8), 128.
  9. Hall, M., Hearn, J. & Lewis, R.. (2022). Digital gender-sexual violations, violence, technologies, motivations. London: Taylor & Francis.
  10. Haney, C. (2003). The psychological impact of incarceration: Implications for postprison adjustment. Prisoners once Removed (p. 33). Urban Institute Press 2003.
  11. Hansen, A. (2022). Relationships, Religion and Robotics: The Soul and the Ethical Implications of AI. (Doctoral dissertation) .
  12. Hartmann, K., & Giles, K. (2020). The next generation of cyber-enabled information warfare. 12th International Conference on Cyber Conflict (CyCon),1300, 233-250. IEEE.
  13. Iacobucci, S., De Cicco, R., Michetti, F., Palumbo, R., & Pagliaro, S. (2021). Deepfakes unmasked: the effects of information priming and bullshit receptivity on deepfake recognition and sharing intention. Cyberpsychology, Behavior, and Social Networking, 24(3), 194-202.
  14. Jones, V. (2020). Artificial intelligence enabled deepfake technology: The emergence of a new threat. (Doctoral dissertation). Utica College.
  15. Karnouskos, S. (2020). Artificial intelligence in digital media: The era of deepfakes. IEEE Transactions on Technology and Society, 1(3), 138-147.
  16. Khichi, M., & Yadav, R.K. (2021). Analyzing the methods for detecting deepfakes. 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) , 340-345.
  17. Kietzmann, J., Lee, L.W., McCarthy, I. P., & Kietzmann, T. C. (2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135-146.
  18. Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2020). Citizens versus the internet: Confronting digital challenges with cognitive tools. Psychological Science in the Public Interest, 21(3), 103-156.
  19. Liu, K., Li, M., Liu, Y., Li, M., Guo, Z., & Hong, F. (2008). Passive diagnosis for wireless sensor networks. Proceedings of the 6th ACM conference on Embedded network sensor systems, 113-126.
  20. Malik, A., Kuribayashi, M., Abdullahi, S.M., & Khan, A.N. (2022). DeepFake detection for human face images and videos: A survey. IEEE Access, 10, 18757-18775.
  21. Masood, M., Nawaz, M., Malik, K.M., Javed, A., Irtaza, A., & Malik, H. (2023). Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward. Applied Intelligence, 53(4), 3974-4026.
  22. Moshayedi, A.J., Roy, A.S., Kolahdooz, A., & Shuxin, Y. (2022). Deep learning application pros and cons over algorithm. EAI Endorsed Transactions on AI and Robotics, 1(1).
  23. Nowroozi, E., Seyedshoari, S., Mohammadi, M., & Jolfaei, A. (2022). Impact of media forensics and deepfake, In Society. Breakthroughs in Digital Biometrics and Forensics, Springer, 387-410.
  24. Paraskevoudis, K., Panagiotis, K., & Elias P. K. (2020). Real-time 3d printing remote defect detection (stringing) with computer vision and artificial intelligence. Processes, 8(11), 1464. International Conference on Research in Engineering, Technology and Science (ICRETS), July 06-09, 2023, Budapest/Hungary
  25. Paterson, T., & Hanley, L. (2020). Political warfare in the digital age: cyber subversion, information operations and 'deep fakes'. Australian Journal of International Affairs, 74(4), 439-454.
  26. Pavis, M. (2021). Rebalancing our regulatory response to Deepfakes with performers' rights. Convergence, 27(4), 974-998.
  27. Rini, R., & Cohen, L. (2022). Deepfakes, deep harms. Ethics & Social Philosophy, 22(1), 143.
  28. Rothstein, M.A., Wilbanks, J.T., Beskow, L.M., Brelsford, K.M., Brothers, K.B., Doerr, M., Evans, B.J., Hammack-Aviran, C.M., McGowan, M.L., & Tovino, S.A. (2020). Unregulated health research using mobile devices: Ethical considerations and policy recommendations. Journal of Law, Medicine & Ethics, 48(51), 196-226.
  29. Sahu, A.K., Umachandran, K., Biradar, V.D., Comfort, O., Sri Vigna Hema, V., Odimegwu, F., & Saifullah, M.A. (2023). A study on content tampering in multimedia watermarking. SN Computer Science, 4(3), 222.
  30. Stroebel, L., Llewellyn, M., Hartley, T., Ip, T.S., & Ahmed, M. (2023). A systematic literature review on the effectiveness of deepfake detection techniques. Journal of Cyber Security Technology, 7(2), 83-113.
  31. Tahir, R., Batool, B., Jamshed, H., Jameel, M., Anwar, M., Ahmed, F., Zaffar, M.A., & Zaffar, M.F. (2021). Seeing is believing: Exploring perceptual differences in deepfake videos. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems.
  32. Traboulsi, N. (2020). Deepfakes: Analysis of threats and countermeasures. (Doctoral dissertation). California State University, Fullerton.
  33. Vasist, P. N.,& Satish Krishnan. (2022). Deepfakes: An integrative review of the literature and an agenda for future research. Communications of the Association for Information Systems, 51(1), 14.
  34. Veerasamy, N., & Pieterse, H. (2022). Rising above misinformation and deepfakes. International Conference on Cyber Warfare and Security.
  35. Wu, L., Morstatter, F., Carley, K.M., & Liu, H. (2019). Misinformation in social media: definition, manipulation, and detection. ACM SIGKDD Explorations Newsletter, 21(2), 80-90.
  36. Yazdinejad, A., Zolfaghari, B., Azmoodeh, A., Dehghantanha, A., Karimipour, H., Fraser, E., Green, A.G., Russell, C., & Duncan, E. (2021). A review on security of smart farming and precision agriculture: Security aspects, attacks, threats and countermeasures. Applied Sciences, 11(16), 7518.
  37. Yu, N., Skripniuk, V., Abdelnabi, S., & Fritz, M. (2021). Artificial fingerprinting for generative models: Rooting deepfake attribution in training data. Proceedings of the IEEE/CVF International Conference on Computer Vision,14448-14457.
  38. Zachary, G. (2020). Digital manipulation and the future of electoral democracy in the US. Digital Manipulation and the Future of Electoral Democracy in the US, 1(2), 104-112.
  39. Zhou, P., Han, X., Morariu, V.I., & Davis, L.S. (2018). Learning rich features for image manipulation detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1053-1061.

Cited by

Sorted by citations

Authors
AL-KHAZRAJI, S. H., SALEH, H. H., KHALID, A. I., & MISHKHAL, I. A.
Affiliations
University of Diyala (IQ)
Venue
The Eurasia Proceedings of Science Technology Engineering and Mathematics
Published
2023-10-16
Volume
23
Pages
429-441
ISSN
2602-3199 (ISSN-L)
OpenAlex
W4387672129
Cited by
98
References
10
Citations by year
2026: 33 · 2025: 46 · 2024: 17 · 2023: 2
FWCI
33.70
Type
journal article
Language
en
Open access
Locations
  • The Eurasia Proceedings of Science Technology Engineering and Mathematics (publishedVersion, cc-by-nc-sa)
  • DergiPark (Istanbul University) (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords
deepfakes, social media, deepfake detection, misinformation, disinformation, public trust, social cohesion, ethical implications, platform policies, content moderation, media manipulation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review