Dublin Core
Title
THE ROLE OF NLP IN DETECTING HATE SPEECH ON SOCIAL MEDIA
Abstract
The accumulation of user-generated content on social media has increased the presence of hate speech and offensive language online, causing serious psychological impact on people. This presents a critical problem for platform moderators and software developers that are trying to create safer online communities. The main purpose of this research is to detect and classify hate speech in social media posts using Natural Language Processing and Machine Learning techniques. By improving the reliability and efficiency of harmful content detection, this research focuses to support efforts in content moderation and reduce the influence of toxic online interactions. The problem was faced using a multi-class classification approach to categorize text into hate speech, offensive language, or neutral speech.
To achieve this, a publicly available dataset of labeled tweets was processed through a detailed pipeline involving data cleaning, normalization, and transformation. Text preprocessing steps such as removing URLs, mentions, hashtags, and punctuation, followed by tokenization, stopword removal, and lemmatization, were essential to reduce noise and standardize the input. The cleaned data was then vectorized using Term Frequency-Inverse Document Frequency (TF-IDF) to represent tweets numerically, enabling machine learning algorithms to extract meaningful patterns.
The preprocessed dataset was used to create and train a number of classification models, including Logistic Regression, Support Vector Machines, Random Forest, Naive Bayes, and many boosting methods like XGBoost, LightGBM, and CatBoost. The Synthetic Minority Over-sampling Technique (SMOTE) was used during training to address class imbalance and improve the model's detection of minority classes, including hate speech.
A broad range of metrics, such as accuracy, precision, recall, F1-score, and confusion matrices, were used to assess performance in order to provide an objective and fair assessment for each class. By demonstrating how NLP and ML techniques may be used to more efficiently detect hate speech, this research supports continuing attempts to automate content moderation. It also lays the foundation for future developments in the discipline by identifying important difficulties in managing complex language and contextual uncertainty.
To achieve this, a publicly available dataset of labeled tweets was processed through a detailed pipeline involving data cleaning, normalization, and transformation. Text preprocessing steps such as removing URLs, mentions, hashtags, and punctuation, followed by tokenization, stopword removal, and lemmatization, were essential to reduce noise and standardize the input. The cleaned data was then vectorized using Term Frequency-Inverse Document Frequency (TF-IDF) to represent tweets numerically, enabling machine learning algorithms to extract meaningful patterns.
The preprocessed dataset was used to create and train a number of classification models, including Logistic Regression, Support Vector Machines, Random Forest, Naive Bayes, and many boosting methods like XGBoost, LightGBM, and CatBoost. The Synthetic Minority Over-sampling Technique (SMOTE) was used during training to address class imbalance and improve the model's detection of minority classes, including hate speech.
A broad range of metrics, such as accuracy, precision, recall, F1-score, and confusion matrices, were used to assess performance in order to provide an objective and fair assessment for each class. By demonstrating how NLP and ML techniques may be used to more efficiently detect hate speech, this research supports continuing attempts to automate content moderation. It also lays the foundation for future developments in the discipline by identifying important difficulties in managing complex language and contextual uncertainty.
Keywords
hate speech detection, natural language processing, machine learning, text classification, content moderation, offensive language, TF-IDF, supervised learning, data preprocessing, XGBoost, Random Forest, LightGBM, SVM, Logistic Regression