## 1 Introduction
However, the limited availability of these resources presents a significant obstacle when it comes to creating models that are highly efficient and widely applicable [10]. Addressing these challenges is vital for improving spam detection methods and maintaining the integrity and user experience of social media platforms [6]. We performed a lot of tests to find the best model-embedding combinations. This answers important questions about how contextualized representations work with various recurrent networks. Therefore, the uniqueness of this research lies in its comprehensive analysis of modern NLP techniques on evolving social media landscapes to advance spam detection systems. We compare these methods with traditional ones to find out how much better they are and how semantically rich embeddings can pick up on subtle spam behaviors on platforms that are getting more complicated. The goal is to make progress in this important area of cybersecurity. Several cybersecurity concerns can be addressed using DL methods and algorithms, including phishing detection, malware identification and classification,intrusion detection, and spam identification. Spam websites are used to spread malware, adult content, and phishing scams, and people spend time sorting through unwanted mail; necessary e-mails may get deleted by mistake. As a result, spam wastes bandwidth, storage space, and computing power, and it promotes a wide range of products and services, resulting in illegal activity. Spam filters have been developed to identify and remove undesirable messages utilizing techniques like keyword analysis and statistical algorithms. Those who participate in spamming often discover methods to outsmart these filters that depend on analyzing content [11]. The constant challenge of dealing with spam has driven the development of increasingly sophisticated techniques for detection and prevention. Spam filters have been developed to identify and remove undesirable messages utilizing techniques such as keyword analysis and statistical algorithms [12; 13]. Spammers often discover methods to outsmart these filters that depend on analyzing content, making it a constant challenge to deal with spam. Increasingly sophisticated techniques for detection and prevention have been developed, such as content-based techniques and DL approaches that examine the terms, frequency, and distribution of words and phrases in message content [14]. Advanced techniques exist to analyze neighbor graphs, taking into account links and metadata to identify and mitigate suspicious nodes and links. Hybrid techniques combine content and metadata to achieve enhanced results, leveraging the strengths of both approaches. These techniques involve analyzing spam text, which brings us into the realm of natural language processing (NLP) [15]. Text classification is a popular application of NLP, as discussed in research studies [16; 17]. The main objective of this research paper is to identify spam by categorizing text as either spam or nonspam (quality). NLP is rapidly evolving; the description of the word in the text to be studied, regardless of the type of problem to be solved, is one of the most essential pillars of NLP. Several ways have emerged to give weight to the word within the text and thus the ability to deal with it as a number that can be processed, compared, and elicit information; this transformation is called word embedding. Static word embedding means that once the words are learned, they do not change with the context. Handling the polysemy problem is challenging with static embeddings due to their unchanging nature. The meaning of a polysemous word can differ depending on the context. Dynamic embeddings, on the other hand, address this issue by assigning varying weights to the same word in different contexts. These dynamic embeddings, which come from language models that have already been trained, have performed better on several natural language processing (NLP) tasks than their static counterparts [18].