## 2 Background and Literature Review
### Word Embeddings Vectors: ELMo and Gloves  
Over the past few years, there has been a significant shift in the field of NLP due to the introduction of groundbreaking linguistic models. In language processing, there is an exciting shift happening away from traditional n-gram models and toward neural networks [41]. These cutting-edge methods combine language modeling with feature learning, which lets words be represented as numerical vectors. This transformation streamlines and enhances data processing, forming the foundation for a powerful technique known as word embeddings. Enhanced representations have been suggested to improve social media spam detection beyond simple word embeddings. These representations merge knowledge graphs and deep learning methods to classify social media posts according to established classification models [41]. Through the process of extracting entities from brief posts and connecting them to concepts in knowledge graphs, contextual knowledge is stored as vector representations. These representations, combined with contextualized word embeddings, are leveraged to create context-based representations of the posts, enhancing the dependability of predictive models. Moreover, a combined method that incorporates data augmentation, natural language processing, and supervised machine learning algorithms has been suggested for identifying Twitter spam. This method utilizes word embedding techniques and various machine learning algorithms like SVM, Naive Bayes, and logistic regression to enhance the identification of Arabic spam on the Twitter platform [41]. Word embedding models use probability distributions to assign vectors to specific words, predicting the likelihood that various tokens appear near the target word. These models are popular in the current NLP community due to their effectiveness in enabling computations using numerical embeddings instead of textual sequences. [PERSON] et al. [42] presented a framework for detecting Arabic spam accounts on Twitter, employing deep learning to analyze both text-based and metadata features, achieving a notable accuracy of 94.27%. This approach outperforms existing models and highlights the importance of combining text and metadata for spam detection. However, the authors used only a specific dataset that may not generalize across different languages or dialects, potential issues with the robustness of spam detection and sarcasm recognition exist across varied contexts, and the challenges of interpreting complex language nuances like irony or local slang could mean that the models' performance could be affected by dynamic changes in social media communication patterns, requiring ongoing adaptation and re-evaluation. [PERSON] et al. [37] introduced a hybrid ensemble model for sarcasm detection, integrating  
Figure 3: LSTM: The third step and the fourth step. fuzzy logic with Word2 Vec, GloVe, and BERT embeddings. It reports high accuracies across different datasets, demonstrating the model's efficacy in understanding the nuanced language of sarcasm on social media. These studies collectively underscore the advancement in utilizing complex models for nuanced social media content analysis, setting a precedent for future research in similar domains. The authors did not address difficulties in interpreting acronyms, slang, or diverse linguistic contexts, highlighting the limitations of using a single NLP approach to handle the wide variety of material encountered on social media platforms. These challenges underscore the need for advanced and adaptable NLP techniques to accurately understand and classify complex language use in social media content. Word embeddings enable the numeric representation of words in a vector space by encoding semantic meaning, which allows textual data to be processed computationally for tasks like classification [43]. Popular techniques like Word2 Vec [44] and GloVe [41] generate embeddings by analyzing the linguistic contexts of words in large corpora.