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@article{182334,
        author = {Thenmozhi Pandian and Neelamalar Maraimalai},
        title = {Understanding Motivations and Patterns Behind Harmful Deepfake and AI-Generated Content: Insights from Virtual Experiences and LDA Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1896-1904},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182334},
        abstract = {Using Latent Dirichlet Allocation (LDA), this study investigates reasons behind creating harmful deepfake and AI-generated content online, focusing on virtual experiences. Results reveal clear themes: novelty, personalized fantasy, enhanced immersion, anonymity, taboo exploration, technology-mediated intimacy, coping, attention-seeking, and profit. Each theme, supported by probability scores, uncovers users' preferences for diverse, stimulating, and personalized content. The following sections analyze these themes, revealing patterns. The discussion explains motivations, weaving together novelty, customization, privacy, and exploration. Understanding this complex user engagement paves the way for future research and interventions. In conclusion, the study calls for legislation addressing ethical and legal aspects, especially concerning leaked content, privacy, and deepfake distribution.},
        keywords = {Deepfakes, Latent Dirichlet Allocation, virtual experiences, user motivations, harmful content},
        month = {July},
        }