Generating a Deepfake Frame
A Text Mining Study Based on Reddit
Copyright (c) 2025 Genlong Zhou, Fei Qiao
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
This study investigates the understanding of deepfake, a highly realistic AI mimicry technique that is rapidly evolving to produce increasingly realistic videos and explores the construction of a deepfake framework through the lens of audience communication using framing theory. It identifies three key findings. First, the public discourse on deepfakes forms a concept hierarchy emphasising technology and its entertainment applications, with core concepts including AI, voice, actor and job, while peripheral concerns such as consent and company receive less focus. Second, employing the BERTopic algorithm, latent themes in public discussions were categorised into two dimensions: social dynamics and cultural phenomena. Third, sentiment analysis reveals predominantly neutral or negative attitudes, indicating concerns over the risks and societal impacts of deepfake technology. The deepfakes framework developed here provides a structured approach to understanding these impacts, highlighting the need for ethical considerations in technological development, regulatory measures and public education.
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References
Ahmed, S. (2021). Fooled by the Fakes: Cognitive Differences in Perceived Claim Accuracy and Sharing Intention of Non-Political Deepfakes. Personality and Individual Differences, 182. Online: https://doi.org/10.1016/j.paid.2021.111074
Akter, S., Hossain, M. A., Sajib, S., Sultana, S., Rahman, M., Vrontis, D. & McCarthy, G. (2023). A Framework for AI-Powered Service Innovation Capability: Review and Agenda for Future Research. Technovation, 125. Online: https://doi.org/10.1016/j.technovation.2023.102768
Aoun, J. E. (2018). Optimism and Anxiety: Views on the Impact of Artificial Intelligence and Higher Education’s Response. Gallup Inc.
Ashok, M., Madan, R., Joha, A. & Sivarajah, U. (2022). Ethical Framework for Artificial Intelligence and Digital Technologies. International Journal of Information Management, 62. Online: https://doi.org/10.1016/j.ijinfomgt.2021.102433
Asimov, I. (1942). Runaround. Street & Smith Publications, Inc.
Auxier, B. & Anderson, M. (2021). Social Media Use in 2021. Pew Research Center.
Bappy, J. H., Simons, C., Nataraj, L., Manjunath, B. S. & Roy-Chowdhury, A. K. (2019). Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries. IEEE Transactions on Image Processing, 28(7), 3286–3300. Online: https://doi.org/10.1109/TIP.2019.2895466
Bastian, M., Heymann, S. & Jacomy, M. (2009, March). Gephi: An Open Source Software for Exploring and Manipulating Networks. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 361–362. Online: https://doi.org/10.1609/icwsm.v3i1.13937
Bates, B. R. (2005). Public Culture and Public Understanding of Genetics: A Focus Group Study. Public Understanding of Science, 14(1), 47–65. Online: https://doi.org/10.1177/0963662505048409
Bonifazi, G., Corradini, E., Ursino, D. & Virgili, L. (2023). Modeling, Evaluating, and Applying the eWoM Power of Reddit Posts. Big Data and Cognitive Computing, 7(1). Online: https://doi.org/10.3390/bdcc7010047
Liao, W. & Al-Qudah, I. (2023). Leveraging State-of-the-Art Topic Modeling for News Impact Analysis on Financial Markets: A Comparative Study. Electronics, 12(12). Online: https://doi.org/10.3390/electronics12122605
Chong, D. & Druckman, J. N. (2007). Framing Theory. Annual Review of Political Science, 10(1), 103–126. Online: https://doi.org/10.1146/annurev.polisci.10.072805.103054
Church, K. W. (2017). Word2Vec. Natural Language Engineering, 23(1), 155–162. Online: https://doi.org/10.1017/S1351324916000334
Claessens, N. & Van den Bulck, H. (2016). A Severe Case of Disliking Bimbo Heidi, Scumbag Jesse and Bastard Tiger: Analysing Celebrities’ Online Anti-Fans. In L. Duits, K. Zwaan & S. Reijnders (Eds.),
The Ashgate Research Companion to Fan Cultures (pp. 63–75). Routledge.
Cleveland, K. (2022). Creepy or Cool? An Exploration of Non-Malicious Deepfakes Through Analysis of Two Case Studies. University of Maryland.
De keersmaecker, J. & Roets, A. (2017). ‘Fake News’: Incorrect, But Hard to Correct. The Role of Cognitive Ability on the Impact of False Information on Social Impressions. Intelligence, 65, 107–
Online: https://doi.org/10.1016/j.intell.2017.10.005
De Vreese, C. H. & Boomgaarden, H. G. (2006). Media Message Flows and Interpersonal Communication: The Conditional Nature of Effects on Public Opinion. Communication Research, 33(1), 19–
Online: https://doi.org/10.1177/0093650205283100
Deas, N., Kowalski, R., Finnell, S., Radovic, E., Carroll, H., Robbins, C., Cook, A., Hurley, K.,Cote, N., Evans, K., Lorenzo, I., Kiser, K., Mochizuki, G., Mock, M. & Brewer, L. (2023). I Just Want to Matter: Examining the Role of Anti-Mattering in Online Suicide Support Communities Using Natural Language Processing. Computers in Human Behavior, 139. Online: https://doi.org/10.1016/j.chb.2022.107499
Domínguez Figaredo, D. & Stoyanovich, J. (2023). Responsible AI Literacy: A Stakeholder-First Approach. Big Data & Society, 10(2). Online: https://doi.org/10.1177/20539517231219958
Drabiak, K., Kyzer, S., Nemov, V. & El Naqa, I. (2023). AI and Machine Learning Ethics, Law, Diversity, and Global Impact. The British Journal of Radiology, 96(1150). Online: https://doi.org/10.1259/bjr.20220934
Egger, R. & Yu, J. (2022). A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in Sociology, 7. Online: https://doi.org/10.3389/fsoc.2022.886498
Entman, R. M. (1993). Framing: Toward Clarification of a Fractured Paradigm. Journal of Communication, 43(4), 51–58. Online: https://doi.org/10.1111/j.1460-2466.1993.tb01304.x
Gamage, D., Ghasiya, P., Bonagiri, V., Whiting, M. E. & Sasahara, K. (2022). Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implications. Proceedings of the 2022
CHI Conference on Human Factors in Computing Systems, New Orleans, USA (pp. 1–19). Online: https://doi.org/10.1145/3491102.3517446
Gamson, W. A. (1992). Talking Politics. Cambridge University Press.
Gamson, W. A. & Modigliani, A. (1987). The Changing Culture of Affirmative Action. Research in Political Sociology, 3(1), 137–177.
Gamson, W. A. & Modigliani, A. (1989). Media Discourse and Public Opinion on Nuclear Power: A Constructionist Approach. American Journal of Sociology, 95(1), 1–37. Online: https://psycnet.apa.org/doi/10.1086/229213
Godulla, A., Hoffmann, C. P. & Seibert, D. (2021). Dealing with Deepfakes – An Interdisciplinary Examination of the State of Research and Implications for Communication Studies. Studies in Communication and
Media, 10(1), 72–96. Online: https://doi.org/10.5771/2192-4007-2021-1-72
Górriz, J. M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J. E., Atzmueller, M., Ballarini, F., Barakova, E., Bologna, G., Bonomini, P., Castellanos-Dominguez, G., Castillo-Barnes, D., Cho, S. B., Contreras, R.,
Cuadra, J. M., Domínguez-Mateos, F., Duro, R. J., Elizondo, D., Fernández-Caballero, A., Fernandez-Jover,
E. & Ferrández-Vicente, J. M. (2023). Computational Approaches to Explainable Artificial Intelligence: Advances in Theory, Applications and Trends. Information Fusion, 100. Online: https://doi.org/10.1016/j.inffus.2023.101945
Gourlet, P., Ricci, D. & Crépel, M. (2024). Reclaiming Artificial Intelligence Accounts: A Plea for a Participatory Turn in Artificial Intelligence Inquiries. Big Data & Society, 11(2). Online: https://doi.org/10.1177/20539517241248093
Hagendorff, T. (2020). The Ethics of AI Ethics: An Evaluation of Guidelines. Minds and Machines, 30(1), 99–120. Online: https://doi.org/10.1007/s11023-020-09517-8
Hancock, J. T. & Bailenson, J. N. (2021). The Social Impact of Deepfakes. Cyberpsychology, Behavior, and Social Networking, 24(3), 149–152. Online: https://doi.org/10.1089/cyber.2021.29208.jth
Heidari, A., Jafari Navimipour, N., Dag, H. & Unal, M. (2024). Deepfake Detection Using Deep Learning Methods: A Systematic and Comprehensive Review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(2). Online: https://doi.org/10.1002/widm.1520
Ho, S. S., Scheufele, D. A. & Corley, E. A. (2013). Factors Influencing Public Risk–Benefit Considerations of Nanotechnology: Assessing the Effects of Mass Media, Interpersonal Communication, and Elaborative
Processing. Public Understanding of Science, 22(5), 606–623. Online: https://doi.org/10.1177/0963662511417936
Huang, M. H. & Rust, R. T. (2021). A Strategic Framework for Artificial Intelligence in Marketing. Journal of the Academy of Marketing Science, 49, 30–50. Online: https://doi.org/10.1007/s11747-020-00749-9
Kneese, T. (2020, November 2). How Data Can Create Full-On Apparitions of the Dead. Slate. Online: https://slate.com/technology/2020/11/robert-kardashian-joaquin-oliver-deepfakes-death.html
Li, S., Xie, Z., Chiu, D. K. & Ho, K. K. (2023). Sentiment Analysis and Topic Modeling Regarding Online Classes on the Reddit Platform: Educators Versus Learners. Applied Sciences, 13(4). Online: https://doi.org/10.3390/app13042250
Li, Y., Su, Z., Yang, J. & Gao, C. (2020). Exploiting Similarities of User Friendship Networks across Social Networks for User Identification. Information Sciences, 506, 78–98. Online: https://doi.org/10.1016/j.ins.2019.08.022
Liu, H. & Priest, S. (2009). Understanding Public Support for Stem Cell Research: Media Communication, Interpersonal Communication and Trust in Key Actors. Public Understanding of Science, 18(6), 704–718. Online: https://doi.org/10.1177/0963662508097625
Lyu, S. (2020). Deepfake Detection: Current Challenges and Next Steps. 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) IEEE. Online: https://doi.org/10.1109/ICMEW46912.2020.9105991
Makarius, E. E., Mukherjee, D., Fox, J. D. & Fox, A. K. (2020). Rising with the Machines: A Sociotechnical Framework for Bringing Artificial Intelligence into the Organization. Journal of Business Research, 120, 262–273. Online: https://doi.org/10.1016/j.jbusres.2020.07.045
Mendonça, M. & Figueira, Á. (2024). Topic Extraction: BERTopic’s Insight into the 117th Congress’s Twitterverse. Informatics, 11(1). Online: https://doi.org/10.3390/informatics11010008
Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A. & Dwivedi, Y. K. (2023). Deepfakes: Deceptions, Mitigations, and Opportunities. Journal of Business Research, 154. Online: https://doi.org/10.1016/j.jbusres.2022.113368
Naitali, A., Ridouani, M., Salahdine, F. & Kaabouch, N. (2023). Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions. Computers, 12(10). Online: https://doi.org/10.3390/computers12100216
Natale, S. & Henrickson, L. (2024). The Lovelace Effect: Perceptions of Creativity in Machines. New Media & Society, 26(4), 1909–1926. Online: https://doi.org/10.1177/14614448221077278
Nguyen, H. N. (2023). Evaluation of Transition State Locating Algorithm Based on Artificial Intelligence. Thesis, B.Sc. (Hons.) in Chemistry, University of Prince Edward Island.
Nisbet, M. C. (2009). Framing Science: A New Paradigm in Public Engagement. In L. Kahlor & P. Stout (Eds.) Communicating Science: New Agendas in Communication. Routledge. Online: https://doi.org/10.4324/9780203867631-10
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau D., Brucher, M., Perrot, M. & Duchesnay, É. (2011). Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. Online: https://doi.org/10.48550/arXiv.1201.0490
Price, V., Nir, L. & Cappella, J. N. (2005). Framing Public Discussion of Gay Civil Unions. Public Opinion Quarterly, 69(2), 179–212. Online: https://doi.org/10.1093/poq/nfi014
Qi, W., Pan, J., Lyu, H. & Luo, J. (2024). Excitements and Concerns in the Post-ChatGPT Era: Deciphering Public Perception of AI Through Social Media Analysis. Telematics and Informatics, 92. Online: https://doi.org/10.1016/j.tele.2024.102158
Reese, S. D. (2001). Framing Public Life: A Bridging Model for Media Research. In S. Reese, O. Gandy & A. Grant (Eds.), Framing Public Life: Perspectives on Media and Our Understanding of the Social World (pp. 7–31). Routledge.
Řehůřek, R. & Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. Conference: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Malta. Online: https://doi.org/10.13140/2.1.2393.1847
Saif, S., Tehseen, S. & Ali, S. S. (2024). Fake News or Real? Detecting Deepfake Videos Using Geometric Facial Structure and Graph Neural Network. Technological Forecasting and Social Change, 205. Online: https://doi.org/10.1016/j.techfore.2024.123471
Scheufele, D. A. (1999). Framing As a Theory of Media Effects. Journal of Communication, 49(1), 103–122. Online: https://doi.org/10.1111/j.1460-2466.1999.tb02784.x
Seymour, M., Riemer, K., Yuan, L. & Dennis, A. (2021). Beyond Deep Fakes: Conceptual Framework, Applications, and Research Agenda for Neural Rendering of Realistic Digital Faces. Communications of the ACM, 66(
, 56–67. Online: https://doi.org/10.1145/3584973
Shen, Q. & Rose, C. (2019). The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit’s Quarantine Policy. In S. T. Roberts, J. Tetreault, V. Prabhakaran, Z. Waseem (Eds.), Proceedings of the Third Workshop on Abusive Language Online (pp. 58–69). Association for Computational Linguistics. Online: https://doi.org/10.18653/v1/W19-3507
Steinbauer, G., Kandlhofer, M., Chklovski, T., Heintz, F. & Koenig, S. (2021). A Differentiated Discussion About AI Education K-12. KI-Künstliche Intelligenz, 35(2), 131–137. Online: https://doi.org/10.1007/s13218-021-00724-8
Stieglitz, S. & Dang-Xuan, L. (2013). Emotions and Information Diffusion in Social Media – Sentiment of Microblogs and Sharing Behavior. Journal of Management Information Systems, 29(4), 217–248. Online: https://doi.org/10.2753/MIS0742-1222290408
Tan, C. & Lee, L. (2015). All Who Wander: On the Prevalence and Characteristics of Multi-Community Engagement. Proceedings of the 24th International Conference on World Wide Web, Florence, Italy (pp. 1056–1066). Online: https://doi.org/10.1145/2736277.2741661
Tang, W., Bu, H., Zuo, Y. & Wu, J. (2024). Unlocking the Power of the Topic Content in News Headlines: BERTopic for Predicting Chinese Corporate Bond Defaults. Finance Research Letters, 62. Online: https:// doi.org/10.1016/j.frl.2024.105062
Ternovski, J., Kalla, J. & Aronow, P. (2022). The Negative Consequences of Informing Voters about Deepfakes: Evidence from Two Survey Experiments. Journal of Online Trust and Safety, 1(2). Online: https://doi.org/10.54501/jots.v1i2.28
Torresen, J. (2018). A Review of Future and Ethical Perspectives of Robotics and AI. Frontiers in Robotics and AI, 4. Online: https://doi.org/10.3389/frobt.2017.00075
Tricomi, P. P., Nenna, F., Pajola, L., Conti, M. & Gamberini, L. (2023). You Can’t Hide Behind Your Headset: User Profiling in Augmented and Virtual Reality. IEEE Access, 11, 9859–9875. Online: https://doi.org/10.1109/ACCESS.2023.3240071
Ulnicane, I., Knight, W., Leach, T., Stahl, B. C. & Wanjiku, W. G. (2021). Framing Governance for a Contested Emerging Technology: Insights from AI Policy. Policy and Society, 40(2), 158–177. Online: https://doi.org/10.1080/14494035.2020.1855800
Valkenburg, P. M., Semetko, H. A. & De Vreese, C. H. (1999). The Effects of News Frames on Readers’ Thoughts and Recall. Communication Research, 26(5), 550–569. Online: https://psycnet.apa.org/doi/10.1177/009365099026005002
Walsh, J. P. (1995). Managerial and Organizational Cognition: Notes from a Trip Down Memory Lane. Organization Science, 6(3), 280–321. Online: https://psycnet.apa.org/doi/10.1287/orsc.6.3.280
Wang, B., Rau, P. L. P. & Yuan, T. (2023). Measuring User Competence in Using Artificial Intelligence: Validity and Reliability of Artificial Intelligence Literacy Scale. Behaviour & Information Technology, 42(9), 1324–1337. Online: https://doi.org/10.1080/0144929X.2022.2072768
Wang, S. & Kim, S. (2022). Users’ Emotional and Behavioral Responses to Deepfake Videos of K-Pop Idols. Computers in Human Behavior, 134. Online: https://doi.org/10.1016/j.chb.2022.107305
West, D. M. (2018, May 21). Brookings Survey Finds Worries Over AI Impact on Jobs and Personal Privacy, Concern the U.S. Will Fall Behind China. Brookings Institution. Online: www.brookings.edu/articles/brookings-survey-finds-worries-over-ai-impact-on-jobs-and-personal-privacy-concern-u-s-will-fall-behindchina/
Westerlund, M. (2019). The Emergence of Deepfake Technology: A Review. Technology Innovation Management Review, 9(11), 40–53. Online: https://doi.org/10.22215/timreview/1282
Whittaker, L., Mulcahy, R., Letheren, K., Kietzmann, J. & Russell-Bennett, R. (2023). Mapping the Deepfake Landscape for Innovation: A Multidisciplinary Systematic Review and Future Research Agenda. Technovation, 125(1). Online: http://dx.doi.org/10.1016/j.technovation.2023.102784
Zaller, J. (1992). The Nature and Origins of Mass Opinion. Cambridge University Press.
Zeng, C., Jian, Y., Vosoughi, S., Zeng, C. & Zhao, Y. (2023). Evaluating Native-Like Structures of RNA-Protein Complexes Through the Deep Learning Method. Nature Communications, 14(1), 1060. Online: https://doi.org/10.1038/s41467-023-36720-9
Zhang, B. & Dafoe, A. (2019). Artificial Intelligence: American Attitudes and Trends. Online: https://dx.doi.org/10.2139/ssrn.3312874
Zhou, Z., Zhou, X., Chen, Y. & Qi, H. (2024). Evolution of Online Public Opinions on Major Accidents: Implications for Post-Accident Response Based on Social Media Network. Expert Systems with Applications, 235. Online: https://doi.org/10.1016/j.eswa.2023.121307
Funding: This research was supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515010670).