Influence of AI-Generated Deepfakes and Synthetic Media on Scientific and Public Consensus: A Case Study of Taraba State, Nigeria

Artificial-intelligence tools capable of producing photorealistic deepfakes and manipulated scientific or health imagery have advanced faster than the institutional, educational, and regulatory mechanisms meant to detect them, raising the question of how such synthetic content shapes scientific and public consensus. This paper offers a structured narrative synthesis of scholarship spanning 2014 to 2026, drawing particular attention to sources from 2020 through 2026, examining how deepfakes and synthetic media influence trust, consensus acceptance, and unaided human detection ability, and situating global findings within Nigeria's information ecosystem. Sources were identified through academic search platforms (including ScienceDirect, Wiley Online Library, PubMed/PMC, and arXiv) and credible Nigerian fact-checking and legal-analysis outlets, screened for relevance to detection accuracy, consensus, or trust outcomes, and synthesized narratively. Across the reviewed literature, unaided human detection of deepfakes consistently approximates chance level and is frequently accompanied by overconfidence, technical labellings and awareness campaigns alone show limited effect, while structured, feedback-based media-literacy training shows the most consistent improvement. Nigerian evidence indicates that fact-checking organizations rely on forensic software rather than unaided judgement to confirm manipulation, that synthetic content has already been used to inflame ethnic and political tension, and that community-anchored trust structures may matter as much as technical verification capacity. The review concludes that addressing deepfake-driven threats to scientific and public consensus in contexts such as Taraba State, Nigeria, requires coordinated institutional, educational, and community-based responses rather than technological solutions alone, and it offers recommendations for tertiary institutions, health authorities, broadcast media, policymakers, and community structures.

deepfakes, synthetic media, scientific consensus, misinformation, digital literacy, science communication, Nigeria

1. Berger, P. L., & Luckmann, T. (1966). The social construction of reality: A treatise in the sociology of knowledge. Doubleday. [Google Scholar] [Crossref]

2. Chesney, R., & Citron, D. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753–1820. [Google Scholar] [Crossref]

3. Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571. https://doi.org/10.1287/mnsc.32.5.554 [Google Scholar] [Crossref]

4. Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. Computers in Human Behavior Reports, 16, 100538. https://doi.org/10.1016/j.chbr.2024.100538 [Google Scholar] [Crossref]

5. Dubawa. (2026, January 6). Six AI-generated deepfakes that deceived Nigerians in 2025. https://dubawa.org/six-ai-generated-deepfakes-that-deceived-nigerians-in-2025/ [Google Scholar] [Crossref]

6. Dubawa. (2025, July 31). AI-generated disinformation looms ahead of Nigeria's 2027 elections. https://dubawa.org/ai-generated-disinformation-looms-ahead-of-nigerias-2027-elections/ [Google Scholar] [Crossref]

7. Erim, A., Oko, S., Biose, S., Agbaoye, K., Nzedibe, O. E., Nwankwo, A., Gimba, P., & Ihekweazu, V. (2025). Tackling infectious disease outbreak and vaccination misinformation: A community-based strategy in Niger State, Nigeria. BMC Health Services Research, 25(1), 513. https://doi.org/10.1186/s12913-025-12683-z [Google Scholar] [Crossref]

8. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680. [Google Scholar] [Crossref]

9. Groh, M., Epstein, Z., Firestone, C., & Picard, R. (2022). Deepfake detection by human crowds, machines, and machine-informed crowds. Proceedings of the National Academy of Sciences, 119(1), e2110013119. https://doi.org/10.1073/pnas.2110013119 [Google Scholar] [Crossref]

10. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840–6851. [Google Scholar] [Crossref]

11. Köbis, N. C., Doležalová, B., & Soraperra, I. (2021). Fooled twice: People cannot detect deepfakes but think they can. iScience, 24(11), 103364. https://doi.org/10.1016/j.isci.2021.103364 [Google Scholar] [Crossref]

12. McCornack, S. A. (1992). Information manipulation theory. Communication Monographs, 59(1), 1–16. https://doi.org/10.1080/03637759209376245 [Google Scholar] [Crossref]

13. Nightingale, S. J., & Farid, H. (2022). AI-synthesized faces are indistinguishable from real faces and more trustworthy. Proceedings of the National Academy of Sciences, 119(8), e2120481119. https://doi.org/10.1073/pnas.2120481119 [Google Scholar] [Crossref]

14. Olisa Agbakoba Legal. (2025/2026). Truth in the age of deepfakes: Legal frontiers against digital deception and image abuse. https://oal.law/truth-in-the-age-of-deepfakes-legal-frontiers-against-digital-deception-and-image-abuse/ [Google Scholar] [Crossref]

15. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hódhegyi, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

16. Schlippe, T., Wölfel, M., & Mabokela, K. R. (2025). A cross-cultural assessment of human ability to detect LLM-generated fake news about South Africa (arXiv:2511.17682). arXiv. https://arxiv.org/abs/2511.17682 [Google Scholar] [Crossref]

17. Somoray, K., Miller, D. J., & Holmes, M. (2025). Human performance in deepfake detection: A systematic review. Human Behavior and Emerging Technologies, 2025(1), Article 1833228. https://doi.org/10.1155/hbe2/1833228 [Google Scholar] [Crossref]

18. TheCable Fact Check. (2025, August 8). The rise of deepfakes and the fight for digital truth. https://factcheck.thecable.ng/the-rise-of-deepfakes-and-the-fight-for-digital-truth/ [Google Scholar] [Crossref]

19. UNESCO. (2025, October 27). Deepfakes and the crisis of knowing. https://www.unesco.org/en/articles/deepfakes-and-crisis-knowing [Google Scholar] [Crossref]

20. Wonodi, C., Obi-Jeff, C., Adewumi, F., Keluo-Udeke, S. C., Gur-Arie, R., Krubiner, C., Jaffe, E. F., Bamiduro, T., Karron, R., & Faden, R. (2022). Conspiracy theories and misinformation about COVID-19 in Nigeria: Implications for vaccine demand generation communications. Vaccine, 40(13), 2114–2121. https://doi.org/10.1016/j.vaccine.2022.02.005 [Google Scholar] [Crossref]

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