Management and Economics
| Open Access |
DOI: Anti-Crisis Communication Strategies in The Era of Deepfakes: Protecting Reputation and Restoring Trust
Ali Hajizada Nizami , CEO of Hajizade Group Washington, D.C., United StatesAbstract
The article presents a comprehensive analysis of anti-crisis communication strategies in the era of AI-generated deepfakes, aimed at identifying effective mechanisms for protecting and managing reputation and restoring public trust. The study is conducted within a theoretical and analytical framework that integrates concepts from cognitive psychology, media linguistics, digital management, and political communication. The analysis is based on recent international publications examining the perception of synthetic media, institutional risks, the influence of lexical formulations on audience anxiety levels, and the role of empathic strategies in managing trust crises. The focus is placed on practical models of response to deepfake-induced crises — proactive, reactive, linguistically adaptive, and systemic. Their cognitive and emotional effects are analyzed, as well as the conditions of their effectiveness depending on response speed, source transparency, and audience media literacy. Particular attention is paid to the cognitive-linguistic determinants of trust restoration — terminological framing, content labeling, empathic narrative, and the “post-deception” phenomenon, which reduces susceptibility to visual evidence even after debunking. The novelty of the study lies in conceptualizing anti-crisis communication as an integrative system combining algorithmic audit, educational practices, and emotionally calibrated dialogue. The proposed approach views communication not as a reaction to a crisis but as a resilient infrastructure of trust based on cognitive credibility, rapid feedback, and the ethics of transparency.
Keywords
trust, communication, deepfakes, perception, reputation, audience, crisis
References
Abraham, T. M., Wen, T., Wu, T., Zhang, Y., & Prakash, B. A. (2025). Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks. Humanities and Social Sciences Communications, 12, Article 1040. https://doi.org/10.1057/s41599-025-05389-4
Ahmed, S., Masood, M., Bee, A. W. T., & Ichikawa, K. (2025). False failures, real distrust: The impact of an infrastructure failure deepfake on government trust. Frontiers in Psychology, 16, Article 1574840. https://doi.org/10.3389/fpsyg.2025.1574840
Barrington, S., Cooper, E. A., & Farid, H. (2025). People are poorly equipped to detect AI-powered voice clones. Scientific Reports, 15, Article 11004. https://doi.org/10.1038/s41598-025-94170-3
De Nadal, L., & Jančárik, P. (2024). Beyond the deepfake hype: AI, democracy, and “the Slovak case”. Harvard Kennedy School Misinformation Review, 5(4). https://doi.org/10.37016/mr-2020-153
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, Article 100538. https://doi.org/10.1016/j.chbr.2024.100538
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), Article e2110013119. https://doi.org/10.1073/pnas.2110013119
Lee, S., & Ben Romdhane, S. (2025). Digital crisis management: How proactive online engagements on patient complaints influence social media users’ perceptions. Frontiers in Communication, 10. https://doi.org/10.3389/fcomm.2025.1564650
Plohl, N., Mlakar, I., Aquilino, L., Bisconti, P., & Smrke, U. (2025). Development and validation of the Perceived Deepfake Trustworthiness Questionnaire (PDTQ) in three languages. International Journal of Human–Computer Interaction, 41(11), 6786–6803. https://doi.org/10.1080/10447318.2024.2384821
Rauchfleisch, A., Vogler, D., & de Seta, G. (2025). Deepfakes or synthetic media? The effect of euphemisms for labeling technology on risk and benefit perceptions. Social Media + Society, 11(3). https://doi.org/10.1177/20563051251350975
Romanishyn, A., Malytska, O., & Goncharuk, V. (2025). AI-driven disinformation: Policy recommendations for democratic resilience. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1569115
Weikmann, T., Greber, H., & Nikolaou, A. (2025). After deception: How falling for a deepfake affects the way we see, hear, and experience media. The International Journal of Press/Politics, 30(1), 187–210. https://doi.org/10.1177/19401612241233539
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Copyright (c) 2025 Ali Hajizada Nizami
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