## 1 Introduction
Effective detection methods strive to enhance the user experience while optimizing information flow. For instance, [PERSON] et al. analyzed spam detection using rule-based methods and machine learning algorithms [20]. At the same time, [PERSON] employed a blend of neural networks and rule-based techniques for the same purpose [21]. Semantic approaches have also been explored in spam detection [22]. Techniques such as Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and labeled LDA have demonstrated improved performance by better representing hidden meanings within documents [23; 24; 25]. These methods aim to identify spam messages by detecting semantic content in texts. Additionally, some researchers have enhanced results by extracting salient topics using semantic schemas [26]. Sentiment analysis contributes to spam classification by evaluating messages' positive or negative polarity, further improving accuracy [27]. Deep learning methods boost performance by representing words in context through word embedding techniques like Doc2 Vec and Word2 Vec [22; 28]. Hybrid models show considerable success in spam detection. [PERSON] et al. developed a hybrid approach combining fuzzy inference and deep learning models, utilizing correlation-based methods and a rule-based genetic search for feature selection [8]. Similarly, [PERSON] et al. proposed a hybrid model based on convolutional neural networks, gated recurrent units, and attention mechanisms, offering lower resource requirements than BERT models [29]. [PERSON] et al. compared various models--including CNN, Naive Bayes, SVM, and LSTM--for message recognition and classification, finding that the LSTM architecture outperformed the others with a 98.4% accuracy rate [30]. Other researchers have focused on semantic-based models and ensemble learning in spam classification. [PERSON] et al. employed a two-level semantic model for spam detection, where emails were first categorized by content using keyword-trained models. Semantic rules were generated to create features representing the spam/non-spam distinction, followed by machine learning classification [31]. [PERSON] et al. assessed the effectiveness of semi-supervised learning approaches for comment spam classification, incorporating NLP methods in the preprocessing and classification steps [32]. Advanced NLP models for spam classification have also been explored in the literature. [PERSON] et al. compared GPT-4, BERT, RoBERTa, and CNN approaches, evaluating their effectiveness in spam detection [33]. [PERSON] and [PERSON] investigated a hybrid machine learning approach to detect phishing and spam emails, achieving a maximum success rate of 94% using multilayer perceptrons on numerical datasets and 99% accuracy with recurrent neural networks and LSTM approaches [34]. [PERSON] introduced a multi-criteria decision-making solution based on the analytic hierarchy process for spam classification, enabling the selection of the most optimal solution among six different machine learners applicable to text and image data [7]. Ensemble learning techniques have further enhanced spam classification efforts. [PERSON] and [PERSON] studied spam detection using GPT-3-based transformers and ensemble learning, vectorizing text messages with various embedding techniques and applying classifiers like SVM, KNN, CNN, and LightGBM, ultimately increasing success rates through ensemble methods [35]. [PERSON] et al. utilized ensemble models, conducting an exploratory data analysis for pattern investigation, feature selection, and classification using random forest and extra trees algorithms [36]. Focusing on optimizing learning strategies, [PERSON] and [PERSON] employed a gated recurrent unit approach for spam detection. They preprocessed message data using NLP methods, vectorized it with TF-IDF, and performed feature extraction, ultimately achieving a classification accuracy of 98.65% with their model [28]. [PERSON] et al.