## 1 Introduction  
Offensive speech contains words or phrases that are used to express contempt for a particular group or humiliate or belittle those who belong to the group [1]. The proliferation of social networks has enabled people to express their emotions, beliefs, and views more freely, making these platforms rich sources of textual data [2]. However, in recent years, offensive language (OL) comments have become increasingly detrimental, posing risks to the quality, popularity, and trust of users on social networks [34]. Consequently, many social network platforms are focused on detecting OL to aid in moderation and removal [5].  
The prevalence of online harassment, both domestically and internationally, has become a significant concern. Initially, human moderators were relied upon to address this issue, but challenges were posed because of scalability, and ethical concerns. As a result, artificial intelligence (AI) and machine learning (ML) have emerged as promising solutions for detecting and mitigating online harassment and hate speech [6, 7]. However, the lack of a standardized definition of hate speech across various groups complicates the classification task. The availability of high-quality, diverse datasets remains a significant challenge in the realm of offensive language detection (OLD) [8]. Offensive language is highly context-dependent, making it difficult for existing models to capture nuances and make accurate predictions [9]. The development of AI/ML systems for OLD raises important questionsabout privacy, free speech, and potential biases in automated moderation [8]. These challenges are further compounded by the vast amount of social media data generated through smartphones, laptops, and tablets, which makes processing, analyzing, and classifying online hate from this unstructured data inefficient due to computational limitations [10] and challenges in developing effective ML and deep learning (DL) models. [PERSON] et al. [11] affirmed the effectiveness of employing n-gram features. [PERSON] et al. [12] incorporated attributes of the user profile such as age and gender alongside text features to improve detection performance. Recently, there has been a growing adoption of DL-based approaches, as seen in the work of [PERSON] et al. [13]. Specifically, methods based on Bidirectional Encoder Representations from Transformers (BERT) have shown promising results in online hate detection tasks, as demonstrated in recent research [14; 15]. The main challenge in offensive speech detection is its highly contextualized nature, which existing work struggles to capture for accurate predictions. This necessitates a detection framework that can effectively capture context to identify offensive speech. To address this challenge, we propose several approaches in our study. First, we add context through the concatenation of features to enhance model understanding. This method combines different types of information to provide a more comprehensive view of the text. Secondly, we experiment with GloVe embeddings to incorporate contextual information at the word level, allowing our models to better understand the nuances of language use. Finally, we utilize BERT-based models, which leverage powerful attention mechanisms to capture context across entire sentences or documents. By employing these diverse strategies, we aim to improve the accuracy and robustness of offensive language detection systems.