## 3 Proposed Methodology
### Dataset  
For our experimentation, we employ the [PERSON] dataset [53], which contains 24,783 labeled tweets. The dataset exhibits an imbalanced class distribution, with 5.77% (1430 tweets) labeled as hate speech, 77.43% (19,190 tweets) as offensive language, and 16.80% (4163 tweets) as neither hate nor offensive. This distribution reflects the real-world  
Figure 1: Abstract overview of our proposed architecture.  
nature of offensive content on social media platforms, where explicit hate speech is less common than general offensive language.  
The dataset consists primarily of English language tweets, including informal language, social media slang, hashtags, mentions, and emoticons typical of Twitter communications. These linguistic characteristics make the dataset particularly challenging and relevant for real-world applications of offensive language detection. Data were collected between 2013 and 2017 through the Twitter API using a lexicon-based sampling approach to ensure the inclusion of potentially offensive content.  
The annotation process was conducted through CrowdFlower, employing multiple annotators per tweet to ensure reliability. The final labels were assigned through majority voting, with interannotator agreement metrics provided to validate the quality of the annotation. This rigorous annotation process helps ensure the reliability of the ground truth labels used in our experiments. Data quality was maintained through several preprocessing steps, including removing duplicates and retweets while preserving the original tweet content. The dataset provides a balanced representation of different types of offensive content, making it suitable for training and evaluating offensive language detection models. Manual verification was performed on a subset of annotations to ensure consistency and accuracy.  
We acknowledge that specific demographic and cultural context information about the tweet authors is not available in the dataset due to privacy considerations, which is a common limitation in social media datasets. However, the diverse nature of Twitter's user base suggests that the dataset captures a broad spectrum of offensive language usage patterns.  
The tweet sample from the dataset shown in Figure 2 presents six distinct columns, each serving a specific purpose in our offensive language detection framework. The primary input for our models is the \"tweet\" column, which contains the raw text data collected from Twitter. These text data go through our comprehensive preprocessing pipeline before feature extraction. The \"count\" column provides frequency information about tweet occurrences, offering insights into data distribution patterns. Three annotation-related columns, \"hate speech\", \"offensive language\", and \"neither\" contain the counts of how annotators labeled each tweet. These columns provide valuable information about annotator agreement and help in understanding the complexity of classification decisions. While not directly used as input features, these annotation counts serve an important role in data quality assessment and analysis of challenging cases. The final column \"class\" serves as our target variable, containing the consolidated classification labels derived from the annotation process. This column uses a numerical encoding where 0 represents hate speech, 1 indicates offensive language, and 2 denotes content that is neither hate speech nor offensive. This target variable is used for both model training and evaluation phases of our experiments.  
Figure 2: Description of the dataset.  
We also analyzed the text length of tweets from all classes, as shown in Figure 3.