## 2 Related Work  
Building upon the discussion in the Section 1, it is essential to explore research trends related to GAI, particularly in the context of security. As GAI technologies, such as ChatGPT, are increasingly integrated into various industries, it is crucial to identify and address the potential issues arising from this integration. Despite the relatively recent emergence of GAI, a significant body of research has already been established, exploring not only the technical capabilities of these models but also their applications across various socio-economic sectors, including tourism, marketing, manufacturing, trade, and education [4]. For instance, [PERSON] et al. (2023) examined the challenges of value judgment and the application of GAI in higher education, emphasizing the complexities of integrating such advanced technologies into academic settings [16]. Similarly, [PERSON] et al. (2023)analyzed the potential benefits of utilizing GAI in educational environments, highlighting its ability to enhance student engagement and enable personalized learning experiences [17]. In the financial and business sectors, [PERSON] et al. (2023) and [PERSON] (2023) investigated how GAI could improve operational efficiency and support decision-making processes, demonstrating its profound impact on industry practices [18; 19]. In healthcare, [PERSON] (2025) utilized insights from ChatGPT to examine the transformative role of mobile health (mHealth) in managing obesity. The study identified trends such as personalized interventions, emerging technologies, remote monitoring, behavioral strategies, and user engagement. Additionally, it highlighted mHealth's potential to improve equity, self-management, and evidence-based practices, while addressing challenges related to privacy and interdisciplinary collaboration [20]. Despite the recognized potential of GAI models like ChatGPT, there is a growing discourse surrounding the ethical and legal challenges arising from their technological limitations. Ethical concerns include biases embedded in training datasets, which may result in skewed or unfair outcomes, as well as the risk of plagiarism due to inaccuracies in citations and sources [21]. Furthermore, the use of AI-generated content raises broader questions about its authenticity and originality. On the legal front, discussions have centered around issues such as authorship recognition, intellectual property rights, and copyright infringements related to content generated by ChatGPT [22]. In the educational context, significant concerns have emerged regarding academic dishonesty, particularly the potential for cheating during exams due to an over-reliance on AI-generated content [23]. From an academic integrity standpoint, there are worries about the misuse of generative AI to produce fabricated data or entirely fictitious research papers, which could severely undermine the credibility and trustworthiness of scholarly work [24]. This study aims to distinguish itself from previous research by employing advanced topic modeling techniques to analyze and visualize the key issues surrounding GAI and security. By uncovering deeper semantic structures within the existing body of research, this approach seeks to provide a more nuanced understanding of the potential opportunities and threats posed by the integration of GAI across industries, with a particular focus on security. Building on the discussion of research trends related to GAI and the importance of analyzing these trends in the context of security, it is essential to explore methods that effectively quantify and evaluate the growing body of literature. While qualitative reviews provide valuable insights, they are limited in their ability to comprehensively assess large volumes of research. To address this, scientometric methods, such as text mining, have emerged as powerful quantitative tools for evaluating the significance of articles and authors in domains like cybersecurity and AI technology [25].