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Prediction model for the dissemination of AI-generated deepfake videos in the intelligent entertainment paradigm
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  • Published: 04 January 2026

Prediction model for the dissemination of AI-generated deepfake videos in the intelligent entertainment paradigm

  • Xiaofei Ma1,
  • Jia Wang1,
  • Enyu Ji1 &
  • …
  • Zhongyu Wang1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing

Abstract

Addressing the risk of uncontrolled dissemination of AI deepfake videos in entertainment scenarios, this study constructs an explainable ensemble learning prediction framework from an entertainment computing perspective, systematically revealing the diffusion mechanisms of technology-enabled entertainment content. Guided by information ecosystem theory, the study first identifies nine core factors influencing deepfake video propagation through multidimensional feature decomposition. It innovatively proposes the RFECV-GA-PSO-RF hybrid feature selection algorithm to achieve efficient dimensionality reduction of entertainment computing features. Subsequently, the study employs a PSO-GA-XGBOOST ensemble model—fusing particle swarm optimization (PSO) and genetic algorithm (GA)—to achieve precise predictions of deepfake video propagation on real-world Chinese video platforms. This approach significantly outperforms existing models, demonstrating average improvements of 42.95% across four evaluation metrics (RMSE reduced to 1.230, MAPE reduced to 0.280, MAE reduced to 1.063, R² reaching 0.818). Finally, leveraging the interpretability of this predictive model, the study quantifies the importance of each feature and feature dimension. The proposed integrated prediction model not only provides novel predictive tools for the field of entertainment computing but also offers quantitative decision support for dissemination regulation and content ecosystem optimization in the era of intelligent entertainment, expanding the theoretical boundaries of interdisciplinary research in entertainment technology.

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Data availability

Data will be made available on request. If needed, please contact Jia Wang via email at wangjia1337@bupt.edu.cn.

References

  1. Du, H. et al. Exploring collaborative distributed diffusion-based AI-generated content (AIGC) in wireless networks. IEEE Netw. 38 (3), 178–186 (2024).

    Google Scholar 

  2. Li, Z. et al. A survey on multimodal deepfake and detection techniques. J. Comput. Res. Dev. 60 (6), 1396–1416 (2023).

    Google Scholar 

  3. Mustak, M. et al. Deepfakes: Deceptions, mitigations, and opportunities. J. Bus. Res. 154, 113368 (2023).

    Google Scholar 

  4. He, K. et al. Cognitive Rashomon effect manufacturing: a case study of deepfake in russia-ukraine conflict. J. Mass. Soc. 1, 88–96 (2023).

    Google Scholar 

  5. Lee, Y. et al. To believe or not to believe: framing analysis of content and audience response of top 10 deepfake videos on YouTube. J. Cyberpsych Beh Soc. N. 24 (3), 153–158 (2021).

    Google Scholar 

  6. O’Donnell, N. Have we no decency? Section 230 and the liability of social media companies for deepfake videos, U. Ill. Law Rev. 2, 701–740 (2021).

    Google Scholar 

  7. Lasswell, H. D. The structure and function of communication in society. Comm. Ideas. 37 (1), 136–139 (1948).

    Google Scholar 

  8. Ni, Y. et al. Prediction of cultural UGC communication effectiveness from the perspective of multi-source heterogeneous data fusion: a combination modeling of GRA-PSO-WRF method. Manag Rev. 36 (11), 235–247 (2024).

    Google Scholar 

  9. Hsieh, J. K., Hsieh, Y. C. & Tang, Y. C. Exploring the disseminating behaviors of eWOM marketing: persuasion in online video. Electron. Commer. Res. 12 (2), 201–224 (2012).

    Google Scholar 

  10. Chaiken, S. Heuristic versus systematic information processing and the use of source versus message cues in persuasion. J. Pers. Soc. Psychol. 39 (5), 752–766 (1980).

    Google Scholar 

  11. Zhou, T., Liu, J. & Deng, S. Online knowledge video dissemination effects based on heuristic-systematic model. J. Mod. Inf. 44 (08), 61–68 (2024).

    Google Scholar 

  12. Fu, S., Su, Y. & Sun, J. Factors that influence the dissemination effects of short videos of refuting rumors based on heuristic-systematic model. J. Chn Soc. Sci. Tech. Inf. 43 (04), 457–469 (2024).

    Google Scholar 

  13. Hu, B. & Feng, C. Influencing factors of propagation effect of science short videos from a cognitive perspective. Stud. Sci. Sci. 41 (10), 1755–1764 (2023).

    Google Scholar 

  14. Ding, D. & Li, X. An empirical study on influencing factors of video dissemination of university library in bilibili. Libr. Inf. Serv. 67 (21), 63–72 (2023).

    Google Scholar 

  15. Cen, C. H. et al. Enhancing the dissemination of Cantonese Opera among youth via bilibili: a study on intangible cultural heritage transmission. Hum. Soc. Sci. Commun. 11 (1), 1038 (2024).

    Google Scholar 

  16. Petty, R. E. et al. The elaboration likelihood model of persuasion, Berkowitz L. advances in experimental social psychology 123–205 (academic, 1986).

    Google Scholar 

  17. Fu, S. & Cheng, Q. Research on the influencing factors of false short video dissemination from the perspective of content emotion: based on CAC and ELM dual models. Inf. Doc. Serv. 46 (02), 61–69 (2025).

    Google Scholar 

  18. Hou, Z. et al. A study on dissemination effect and influencing factors of short health education videos. Chn J. Health Educ. 40 (10), 913–918 (2024).

    Google Scholar 

  19. Liu, J. F., Lu, C. Y. & Lu, S. J. H. Research on the influencing factors of audience popularity level of COVID-19 videos during the COVID-19 pandemic. Healthcare 9 (9), 1159 (2021).

    Google Scholar 

  20. Ma, X. et al. Video popularity prediction model based on attention and neural network. J. Hefei Univ. Technol. Nat. Sci. 46 (11), 1472–1478 (2023).

    Google Scholar 

  21. Zhong, Z. et al. Modeling dynamics of online short video popularity based on Douyin platform. J. Univ. Electron. Sci. Technol. China. 50 (05), 774–781 (2021).

    Google Scholar 

  22. Brewer, S. M., Kelley, J. M. & Jozefowicz, J. J. A blueprint for success in the US film industry. Appl. Econ. 41 (5), 589–606 (2009).

    Google Scholar 

  23. Zhang, W. & Skiena, S. S. Improving Movie Gross Prediction through News Analysis,2009 IEEE/WIC/ACM Int. Conf. on Web Intel. WI 2009 Milan Italy September 2009 Main Conference Proceedings 15–18. (2009).

  24. Dellarocas, C., Zhang, X. M. & Awad, N. F. Exploring the value of online product reviews in forecasting sales: The case of motion pictures. J. Interact. Mark. 21(4), 23–45 (2007).

    Google Scholar 

  25. Cho, M., Jeong, D. & Park, E. Predicting popularity of short-form videos using multi-modal attention mechanisms in social media marketing environments. J. Retail Consum. Serv. 78, 103778 (2024).

    Google Scholar 

  26. Wu, W. et al. Deep attention video popularity prediction model fusing content features and Temporal information. J. Comput. Appl. 41 (7), 1878–1884 (2021).

    Google Scholar 

  27. Li, J., Guan, H. & Zhang, S. Modeling on playback volume prediction of self-produced programs of Chinese video websites. J. Inf. Commun. 24 (6), 7–20 (2017).

    Google Scholar 

  28. Zhu, H. et al. Study of short video popularity prediction based on network representation learning. J. Chn Soc. Sci. Tech. Inf. 43 (09), 1105–1115 (2024).

    Google Scholar 

  29. Lin, Y. T., Yen, C. C. & Wang, J. S. Video popularity prediction: an autoencoder approach with clustering. IEEE Access. 8, 129285–129299 (2020).

    Google Scholar 

  30. Halim, Z., Hussain, S. & Ali, R. H. Identifying content unaware features influencing popularity of videos on youtube: A study based on seven regions,Expert. Syst. Appl. 206, 117836 (2022).

    Google Scholar 

  31. Sangwan, N. & Bhatnagar, V. A framework for video popularity forecast utilizing metaheuristic algorithms. Arab. J. Sci. Eng. 47 (2), 2077–2094 (2022).

    Google Scholar 

  32. Chang, N. et al. An empirical study on the trigger mechanism of public opinion communication of hot events in social media. Inf. Sci. 41 (11), 120–127 (2023).

    Google Scholar 

  33. Dou, Y. et al. Research on the influence of dissemination and interaction of public security affairs Douyin accounts based on information ecology theory and algorithm recommendation. Oper. Res. Manage. Sci. 33 (5), 9–15 (2024).

    Google Scholar 

  34. Xu, X. & Zhao, Z. Research on demand characteristics and participation behavior of intangible cultural heritage information of short video users——taking Huangmei Opera short video online review as an example. J. Mod. Inf. 42 (8), 74–84 (2022).

    Google Scholar 

  35. Wang, C. & Mang, L. How government short videos gain viral impact: a content analysis of government Douyin accounts. E-Gov 7, 31–40 (2019).

    Google Scholar 

  36. Huang, C. The development status and trend of short videos under the integration background. Front 23, 40–47 (2017).

    Google Scholar 

  37. Welbourne, D. & Grant, W. Science communication on youtube: factors that affect channel and video popularity. Public. Underst. Sci. 25 (6), 706–718 (2016).

    Google Scholar 

  38. Zhao, L. The social construction of algorithmic practice: take an information distribution platform as an example. Sociol. Stud. 37 (4), 23–44 (2022).

    Google Scholar 

  39. Yang, D., Li, S. & Cong, Y. Research on influencing factors of transmission effect of reading promotion short videos on TikTok. Res. Libr. Sci. 23, 34–44 (2021).

    Google Scholar 

  40. Liu, G. & Wang, X. The influence of title features on the effect of digital media content communication——based on an empirical study of WeChat official accounts title in news commentary. J. Commun. Rev. 73 (6), 29–39 (2020).

    Google Scholar 

  41. Kui, J., Wang, L. & Liu, Y. A study on factors influencing users’ book purchase intentions via short videos. Chn Publ J. 6, 8–14 (2020).

    Google Scholar 

  42. Zhang, Y. et al. Empirical study on influencing factors of communication effect of scientific journal video on bilibili. Chn J. Sci. Tech. Period. 35 (08), 1125–1133 (2024).

    Google Scholar 

  43. Shen, H. et al. Research on the dissemination effectiveness of emergency knowledge short videos:an analysis based on different types of publishers,Inf. Stud. Theor. Appl. 47 (11), 101–110 (2024).

    Google Scholar 

  44. Ning, H. & Yang, W. Empirical analysis of factors influencing the communication effectiveness of major public health emergencies: a case study of health-related government accounts on Douyin. Mod. Commun. 43 (1), 147–151 (2021).

    Google Scholar 

  45. Meng, S. et al. Research on the impact of COVID-19 science information dissemination on social media and the factors influencing the choice of crisis coping strategies:an empirical analysis based on popular micro-blog texts of scientists’ groups. Libr. Inf. Serv. 66 (13), 91–101 (2022).

    Google Scholar 

  46. Liu, C., Chen, M. & Yi, L. Information features extraction and the correlation calculation of the deep fake videos. J. Intell. 43 (8), 92–101 (2024).

    Google Scholar 

  47. Jiang, J. & Wang, W. Research on government Douyin for public opinion of public emergencies: comparison with government microblog. J. Intell. 39 (1), 100–106 (2020).

    Google Scholar 

  48. Yang, H., Li, X. & Hu, Z. Survey of deepfake face generation and detection technologies. J. Huazhong Univ. Sci. Technol. 53 (05), 85–103 (2025).

    Google Scholar 

  49. Krippendorff, K. Agreement and information in the reliability of coding. Commun. Methods Meas. 5 (2), 93–112 (2011).

    Google Scholar 

  50. Chen, W. & Zhou, Y. An empirical study on factors influencing dissemination effect of short videos in popular science journals in china: focusing on 50 Chinese outstanding popular science journals in 2020. Chn J. Sci. Tech. Period. 34 (12), 1616–1622 (2023).

    Google Scholar 

Download references

Acknowledgements

This research was funded by the Social Science Foundation of Beijing, China(NO.25JCC128).

Author information

Authors and Affiliations

  1. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xiaofei Ma, Jia Wang, Enyu Ji & Zhongyu Wang

Authors
  1. Xiaofei Ma
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  2. Jia Wang
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  3. Enyu Ji
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  4. Zhongyu Wang
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Contributions

Xiaofei Ma: Writing - review & editing, Writing - original draft, Conceptualization.Jia Wang: Writing - review & editing, Writing - original draft, Validation, Methodology, Conceptualization.Enyu Ji: Writing - review & editing, Conceptualization.Zhongyu Wang: Writing - review & editing, Conceptualization.

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Correspondence to Jia Wang.

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Cite this article

Ma, X., Wang, J., Ji, E. et al. Prediction model for the dissemination of AI-generated deepfake videos in the intelligent entertainment paradigm. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34789-4

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  • Received: 17 November 2025

  • Accepted: 31 December 2025

  • Published: 04 January 2026

  • DOI: https://doi.org/10.1038/s41598-025-34789-4

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Keywords

  • AI deepfake videos
  • Video dissemination prediction
  • Multi-model ensemble
  • Entertainment computing
  • Explainable machine learning
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