Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models

Abstract

This paper reports an increment to the state-of-the-art in hate speech detection for English-Hindi code-mixed tweets. We compare three typical deep learning models using domain-specific embeddings. On experimenting with a benchmark dataset of English-Hindi code-mixed tweets, we observe that using domain-specific embeddings results in an improved representation of target groups, and an improved F-score.

This paper will appear at the 15th International Conference on Natural Language Processing (ICON-2018) in India in December 2018. ICON is a premier NLP conference in India

References in corpus (1)

• Automated Hate Speech Detection and the Problem of Offensive Language

Cited by in corpus (8)

• An Online Multilingual Hate speech Recognition System
• SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
• Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data
• Hate and Offensive Speech Detection in Hindi and Marathi
• Challenges and Considerations with Code-Mixed NLP for Multilingual Societies
• IIITG-ADBU@HASOC-Dravidian-CodeMix-FIRE2020: Offensive Content Detection in Code-Mixed Dravidian Text
• Cross-lingual hate speech detection based on multilingual domain-specific word embeddings
• Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language