BERT-OTA: Enhancing Hate Speech Detection With Ontology-Guided Transformer Attention
Mahmoud Abusaqer, Mukulika Ghosh, Jamil Saquer
IEEE Access
Problems Identified (4)
Hate speech detection challenge: Social media hate speech creates a significant content moderation challenge requiring methods that understand explicit and implicit harmful content.
Contextual nuance limitations: Traditional hate speech detection approaches struggle with contextual nuances and evolving online hate speech patterns.
Hate speech detection challenge: Social media hate speech creates a significant content moderation challenge requiring methods that understand explicit and implicit harmful content.
Contextual nuance limitations: Traditional hate speech detection approaches struggle with contextual nuances and evolving online hate speech patterns.
Proposed Solutions (4)
BERT-OTA: The paper proposes BERT-OTA, an ontology-guided transformer attention architecture for hate speech detection.
Dual-stream BERT-GCN architecture: The approach processes text with BERT and scaled dot-product attention while learning ontological features with a two-layer Graph Convolutional Network.
BERT-OTA: The paper proposes BERT-OTA, an ontology-guided transformer attention architecture for hate speech detection.
Dual-stream BERT-GCN architecture: The approach processes text with BERT and scaled dot-product attention while learning ontological features with a two-layer Graph Convolutional Network.
Results (3)
State-of-the-art hate speech detection performance:
Structured knowledge improves detection:
State-of-the-art hate speech detection performance:
Research Domain
Hate speech detection / online content moderation