So-haTRed: A Novel Hybrid System for Turkish Hate Speech Detection in Social Media With Ensemble Deep Learning Improved by BERT and Clustered-Graph Networks

Abstract

Hate speech on online platforms, characterized by discriminatory language targeting individuals or groups, poses significant harm and necessitates robust detection methods for digital safety. Recognizing the ease with which individuals can engage in such speech online, our study delved into detecting Turkish hate speech using deep learning algorithms and natural language processing techniques. We developed innovative methodologies, including a k-means+textGCN classifier with BERT, which marked the first such attempt in the literature, and explored multiple vector representation techniques such as Term Frequency, Word2Vec, Doc2Vec, and GloVe. Additionally, we investigated various learning algorithms and natural language processing techniques, conducting thorough evaluations on three distinct Turkish hate speech datasets. Notably, our newly presented algorithm exhibited superior performance, achieving an impressive F1-score of 87.81% on the 9K dataset, showcasing advancements in hate speech detection and contributing to a safer online environment.

Keywords

• Graph convolutional network
• hate speech detection
• machine learning
• natural language processing
• toxic speech
• Turkish social media