## 3 Materials and Methods
### Research Method
#### 3.2.4 Concept of LDA Topic Modeling  
LDA was employed as the primary technique for topic modeling in this study. LDA is particularly effective when articles tend to converge on certain focal points, making it a preferred method for extracting latent topics from large corpora. It is widely used in research across various domains, including academic papers, news articles, and patents [39]. To derive meaningful insights from LDA, certain parameters must be specified, particularly the alpha and beta values, as well as the number of topics to be generated. The alpha parameter controls the distribution of topics across documents, while the beta parameter governs the distribution of words within topics. These parameters typically range from 0.01 to 0.99, and their values are fine-tuned to optimize the model [40]. Additionally, selecting the appropriate number of topics is a critical challenge in LDA topic modeling, which is addressed by the coherence score. This metric helps determine the optimal number of topics and the relevance of keywords within each topic [41]. The coherence score is used as a proxy for the interpretability and consistency of the topic model. It calculates how frequently words within each topic co-occur in the dataset, assessing the coherence of topics [32]. To determine the optimal number of topics and parameter settings in this study, the alpha and beta values were varied between 0.01 and 0.99, and the number of topics was tested from two to ten, considering the total number of articles in the dataset. The coherence index was calculated for each configuration, enabling comparisons across different topic compositions. Through this comparison, the study identified core topics--those with a high density of consistent keywords--and variable topics, which showed fluctuations based on the model settings. The analysis also involved interpreting the meanings and implications of the subject areas represented by each topic, providing deeper insights into the thematic structure within the field of GAI and security. Figure 4 illustrates the conceptual model of the LDA topic modeling process used in this study. Figure 4 illustrates the probabilistic graphical model of latent Dirichlet allocation (LDA), which represents the generative process of topic modeling [39]. * \(\alpha\) (alpha): Dirichlet prior controlling the distribution of topics across documents. * \(\theta\) (theta): Topic distribution for each document, drawn from a Dirichlet prior \(\alpha\). * \(\beta\) (beta): Dirichlet prior governing the distribution of words within topics. * z (zeta): Latent topic assigned to each word in a document, sampled from \(\theta\). * w (w): Observed word in a document, generated based on z and \(\beta\). * M (corpus size): Total number of documents. * N (document length): Number of words in a document. The outer plate (M) represents documents, and the inner plate (N) represents words within a document. The arrows depict the generative process: each document is modeled as a mixture of topics (\(\theta\)), and each word is drawn from a topic's word distribution (\(\beta\)) based on the assigned topic (z). This process enables LDA to infer latent topics in a corpus. One of the most challenging aspects of LDA topic modeling is determining the optimal number of topics [42]. To address this, the coherence score was used as a reference index, specifically employing the \(C_{v}\) metric. The \(C_{v}\) metric has been shown to perform effectively in assessing the interpretability of extracted topics, based on the pairwise similarity between words within a topic [43]. This metric assumes that higher pairwise similarity between words within a topic corresponds to greater coherence. The \(C_{v}\) metric conceptualizes a keyword as a context vector, representing its co-occurrence frequency with surrounding terms. It calculates the cosine similarity between these vectors and averages the pairwise similarities to produce the coherence score. The formula for calculating the \(C_{v}\) value is provided in Equation (1) [44].