Understanding and Detecting Dangerous Speech in Social Media
Not on the map. Why?
Abstract (arXiv)
Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our dataset to develop highly effective models to detect dangerous content. Our best model performs at 59.60% macro F1, significantly outperforming a competitive baseline.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 May 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 3; 2025: 1.
12 papers.
- 2022ORCA: A Challenging Benchmark for Arabic Language Understandingcited by 46
- 2023GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLPcited by 37
- 2024Arabic Dataset for LLM Safeguard Evaluationcited by 17
- 2022JASMINE: Arabic GPT Models for Few-Shot Learningcited by 11
- 2021Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labelingcited by 6
- 2021Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approachcited by 6
- 2025MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMscited by 4
- 2023The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languagescited by 3
- 2023Toxic language detection: a systematic review of Arabic datasetscited by 3
- 2022A Benchmark Study of Contrastive Learning for Arabic Social Meaningcited by 2
- 2024On the importance of Data Scale in Pretraining Arabic Language Modelscited by 0
- 2024Posts of Peril: Detecting Information About Hazards in Textcited by 0
- 2025MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs26 May 2025
- 2024Arabic Dataset for LLM Safeguard Evaluation22 Oct 2024
- 2024Posts of Peril: Detecting Information About Hazards in Text28 May 2024
- 2024On the importance of Data Scale in Pretraining Arabic Language Models15 Jan 2024
- 2023Toxic language detection: a systematic review of Arabic datasets12 Dec 2023
- 2023The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages23 Oct 2023
- 2023GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP24 May 2023
- 2022ORCA: A Challenging Benchmark for Arabic Language Understanding21 Dec 2022
- 2022JASMINE: Arabic GPT Models for Few-Shot Learning21 Dec 2022
- 2022A Benchmark Study of Contrastive Learning for Arabic Social Meaning22 Oct 2022
- 2021Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach27 Nov 2021
- 2021Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling12 Jan 2021