## 1 Introduction  
The proliferation of spam has evolved from basic email campaigns to widespread promotion of content across online social networks (OSNs). Fake accounts are created en masse to disseminate spam posts and manipulate self-promotion [1]. Unlike email, OSN posts contain multimedia--images, videos, URLs--alongside text. Generating impactful posts demands significant effort; hence, spammers aggressively reuse content to target more users. However, most detection methods focus narrowly on post text, account activity patterns, and simplistic metadata like followers. Keyword frequency analysis techniques are easily fooled by word stuffing without considering semantic coherence, posting times, or languages used [2]. After closely examining the limitations of existing methods against evolving bot tactics, we identified the lack of robust feature engineering as a core research gap. Empirical studies show over 30% of social media content is estimated as spam--far higher engagement than email [3]. Spam click-through rates exceeding 0.13% demonstrate the scale and sophistication of organized campaigns tailored to exploit OSN vulnerabilities [4]. This ever-adapting threat landscape poses significant challenges for identification methods to keep pace. Our work looks to bridge these gaps through state-of-the-art natural language processing techniques for enriched semantic modeling--a crucial capability for uncovering disguised and context-aware spam. We focus on robust representation learning rather than superficial features to tackle the root cause. This issue has a significant impact on the user experience and can be challenging to deal with due to the ever-changing nature of new tactics and the increasing complexity of evolving spam. Therefore, the current DL methods are facing challenges in identifying social spam and keeping up with the ever-growing complexity of evolving spam [5]. The most proficient spam campaigns have a notable economic aspect that engages in questionable tactics to boost website traffic and generate false promotional results, leading to significant financial repercussions that present a multi-faceted threat that impacts internet users globally, and search engine optimization (SEO) can be seen clearly, posing challenges for providers of these services and affecting search results too. Dealing with spam on social media is a challenging task due to the constantly evolving tactics used by spammers. In this dynamic landscape, the fight against spam detection becomes an ongoing challenge that requires a continuous adaptation of defensive strategies to keep up with the sophisticated and inventive tactics employed by spammers [6]. Traditional word vectors often struggle to capture the complexities and different meanings words can have in different situations, which is crucial for identifying subtle and cleverly disguised spam. Accurately modeling the semantic context can be quite challenging when using these vectors [7; 8]. DL models that can seamlessly adapt to different social media platforms present their own set of complexities, unique characteristics, and user behaviors, requiring models that can easily adapt and be flexible. This study provides a novel comparative analysis of state-of-the-art contextualized word embeddings (BERT, ELMo) against traditional noncontextualized word vectors (Word2 Vec, GloVe) for spam detection on social media platforms. Unlike previous works, we evaluate these advanced NLP models on two distinct datasets: Twitter and YouTube comments. We looked into an area that has not been studied much: How well contextualized embeddings work on informal, noisy textual data from social networks. We also compared standalone BERT and ELMo models against RNN-based (LSTM) architectures for spam classification. There is a lack of comprehensive labeled datasets available for training adaptable spam detection models on various platforms, which are essential to training and improving spam detection algorithms [9].