Arabic Offensive Language on Twitter: Analysis and Experiments

Abstract

Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers use offensive language. Lastly, we conduct many experiments to produce strong results (F1 = 83.2) on the dataset using SOTA techniques.

10 pages, 6 figures, 3 tables

References in corpus (6)

• SQuAD: 100,000+ Questions for Machine Comprehension of Text
• AraBERT: Transformer-based Model for Arabic Language Understanding
• GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
• RACE: Large-scale ReAding Comprehension Dataset From Examinations
• SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
• Detecting Hate Speech in Social Media

Cited by in corpus (10)

• Pre-Training BERT on Arabic Tweets: Practical Considerations
• SOLD: Sinhala Offensive Language Dataset
• WOLI at SemEval-2020 Task 12: Arabic Offensive Language Identification on Different Twitter Datasets
• NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer
• Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models
• Cross-lingual Inductive Transfer to Detect Offensive Language
• Voice@SRIB at SemEval-2020 Task 9 and 12: Stacked Ensembling method for Sentiment and Offensiveness detection in Social Media
• Exploratory Arabic Offensive Language Dataset Analysis
• Automatic Expansion and Retargeting of Arabic Offensive Language Training
• UPB at SemEval-2020 Task 12: Multilingual Offensive Language Detection on Social Media by Fine-tuning a Variety of BERT-based Models