Investigating Hate Speech Beyond Detection and Classification: Uncovering Complex Intensities and Targets

Hate speech studies often focus on binary categories, neglecting its nuanced spectrum. This research introduces a dataset of 8,258 Amharic tweets annotated for category classification, hate target identification, and ratings of intensities. Most tweets are found to be less offensive, highlighting the need for early interventions. The dataset reveals significant ethnic and political hatred overlaps, reflecting Ethiopia's complex sociopolitical dynamics. The study shows that hate speech requires continuous analysis rather than simplistic classifications. The Afro-XLMR-large model achieved an F1 score of 75.30% for category classification, demonstrating effective performance in this area.