SOCIAL MEDIA-BASED MULTI-CLASS CLASSIFICATION MODEL FROM AMHARIC HATE SPEECH TEXT USING DEEP LEARNING

Abstract

The proliferation of hate speech on social media poses significant challenges to social cohesion and stability, particularly in Ethiopia. This research investigates approaches to detecting and classifying Amharic hate speech on Facebook using deep learning techniques. To address these challenges, this study developed a multi-class hate speech detection system focusing on three critical categories: ethnic, political, and religious hate speech. Using a comprehensive dataset of 4,067 Facebook posts from pages with over 50,000 followers, the study manually categorized content into three hate speech types: ethnic (1,497 posts), political (1,320 posts), and religious (1,250 posts). The study employed two deep learning models, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (Bi-LSTM) networks, to analyze and classify hate speech. The dataset underwent meticulous preprocessing through tokenization, text cleaning, and normalization techniques to ensure data quality. This study evaluates the effectiveness of Bi-LSTM and CNN deep learning models in classifying Amharic hate speech into ethnic, political, and religious categories. The Bi-LSTM model outperformed CNN, achieving a weighted average precision and recall of 0.83 and overall accuracy of 0.83, compared to CNN's 0.80 across all metrics. While both models demonstrated strong performance, Bi-LSTM showed superior capability in capturing contextual information and maintaining consistent classification accuracy across categories. Moreover, the study highlighted potential challenges in the practical implementation of deep learning-based hate speech detection systems, such as managing code-switching, adapting to evolving language patterns, and ensuring fairness and transparency. Therefore, the study recommended that collaboration with various stakeholders is crucial for the successful implementation and continuous improvement of the system. This includes working with social media platforms, government agencies, and civil society organizations to integrate the models into content moderation pipelines and policy enforcement frameworks.