## 1 Introduction
broke new ground in their study by employing advanced artificial intelligence techniques to tackle the persistent issue of spam detection on social media platforms like Twitter and YouTube. Their innovative methods, which include recurrent neural networks with long- and short-term memory cells and static and contextual word embedding techniques, were specifically designed for these platforms. The study revealed that platform-specific AI techniques, particularly deep learning (DL) architectures, significantly enhance performance by leveraging both intrinsic platform contexts and extrinsic contextual embedding techniques. The results demonstrated a sustained accuracy improvement of 10-15% on multi-source datasets. Notably, the study found that contextual embedding techniques such as Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) outperformed traditional methods. For instance, the ELMo model achieved an impressive 90% accuracy for Twitter and 94% accuracy for YouTube when used with logistic regression [37]. In their study, [PERSON] et al. proposed a dynamic deep ensemble model for spam detection. It is stated that the proposed model can adjust its complexity and extract features automatically. The model uses random forest and extremely randomized trees, Boosting, and Bagging algorithms with convolutional and pooling layers to classify texts according to their spam status. The developed method is called Deep Convolutional Forest (DCF). While evaluating the model's performance on the spam messages in the UCI repository, the SMOTE oversampling technique was used for cases where the class distribution is unbalanced. The study achieved a maximum accuracy of 98.38%for UCI SMS spam data [38]. [PERSON] et al. developed a transformer model that proved to be highly effective in spam detection, as demonstrated by its impressive accuracy rates. The model, which was specifically tailored for spam detection, first transforms messages into embedding vectors and adds location information to these vectors for SMS spam detection. These processed vectors are then sent to the encoder layers, where a self-attention mechanism is implemented. The encoder outputs are further analyzed by the decoder layers using Multi-Head Attention. The decoded vectors then produce a final result for classification through fully connected linear layers. This structure proved to be highly successful in determining whether messages are spam or not. The study reported an accuracy of 98.92% on the SMS dataset and 87.06% on the UtkMl Twitter dataset [39]. In their study, [PERSON] et al. proposed a deep learning-based model called TextSpamDetector for spam detection. It is stated that the model includes both local semantic features and global dependency features thanks to the structural context representation created using CNN and BiLSTM. The TextSpamDetector algorithm detects spam by taking a variable-length dataset of sentences. First, embedding representations are created for each sentence, and then the local representation is enriched with past and future contexts using bidirectional LSTM. Informative word contexts are obtained by applying normal and context-preserving attention mechanisms, and these representations are combined to create the final sentence representation. Finally, this feature vector is applied to the softmax layer to determine the class labels of the sentences. Using three datasets, the proposed model is compared with 16 different spam detection techniques. Accuracy values between 78.59% and 99.43% were achieved in the datasets [40]. In summary, the persistent threat of spam messages necessitates the continuous research and development of sophisticated detection methods. By integrating various techniques--from rule-based systems to advanced deep learning models--researchers aim to enhance detection accuracy, reduce false positives, and safeguard users from the multifaceted dangers posed by spam.